Search Results (621)

Infrared (IR) and visible image fusion aims to synthesize a composite representation that integrates the thermal target saliency of IR imagery with the textural richness of visible imagery. Existing deep learning-based methods have achieved promising progress in this field. However, they either operate at the pixel level without semantic priors, or rely on segmentation supervision to obtain such priors. Both approaches limit their practicality and performance in complex scenes. To design a lightweight fusion network that leverages semantic priors without segmentation supervision, we propose SPE2Fusion, a semantic prior-driven fusion network that operates through a dual-stage semantic injection paradigm. Specifically, a lightweight semantic encoder is designed to extract multi-scale scene priors in an end-to-end manner optimized solely by the fusion loss, without requiring segmentation mask annotations. Then, these priors are injected at two complementary stages: the Efficient Semantic Feature Awareness (ESFA) module applies spatially adaptive attention at the encoding stage to amplify semantically salient regions, while the Efficient Semantic Feature Embedding (ESFE) module applies semantically conditioned spatial normalization at the decoding stage to ensure coherent texture reconstruction. Finally, a bidirectional cross-attention fusion block is introduced to integrate complementary cross-modal features under this dual semantic guidance. The network is supervised by a multi-constraint loss combining gradient fidelity, intensity preservation, and structural similarity terms. Comprehensive experiments on the MSRS, LLVIP, and RoadScene benchmarks demonstrate that SPE2Fusion achieves state-of-the-art performance against representative methods (e.g., CrossFuse and DDBFusion), ranking first on four of six metrics on the MSRS test set, specifically EN (6.70), $Q^{AB/F}$ (0.86), AG (6.06), and SD (43.44), while maintaining strong generalization on unseen datasets without domain adaptation. Full article

With the rapid expansion of the unmanned retail ecosystem, real-time foreign object detection (FOD) in smart vending cabinets has become a critical technology for ensuring equipment safety and protecting user rights. However, existing models often face bottlenecks in accuracy when dealing with small targets and occlusion scenarios, and struggle to balance accuracy with speed on edge devices. To address these challenges, this paper proposes an improved model specifically designed for foreign object detection based on the YOLOv11n framework, named YOLOv11n-FOD (foreign object detection). In terms of algorithm design, this paper reconstructs the feature extraction and fusion paradigm. Specifically, the original C3K2 module in the backbone network is replaced with a C3K2-SAC (Spatial Attention Convolution) module incorporating an attention mechanism, which enhances global context modeling capabilities. Subsequently, the CARAFE (Content-Aware ReAssembly of Features) operator is introduced to replace traditional interpolation, significantly improving sensitivity to small targets and textural details. Furthermore, the CBAM (Convolutional Block Attention Module) is integrated into the downsampling stage to suppress background noise while reducing computational redundancy. Notably, these improvements maintain an extremely lightweight architecture, increasing computational overhead by only 0.3 GFLOPs. Experimental results demonstrate that the proposed YOLOv11n-FOD achieves significant performance gains: mAP@50 is increased by 0.4%, and mAP@50-95 is improved by 1.0%. Extensive experiments on the SKU-110K dataset further verify the superior performance of the proposed model. In conclusion, this study effectively balances detection accuracy, model complexity, and inference speed, providing an efficient solution for foreign object detection in smart retail cabinets. Full article

Underwater laser range-gated imaging (ULRGI) effectively suppresses water backscattering via time-slicing mechanisms, making it a primary modality for underwater vision. However, factors such as the inherent optical properties of water, intra-slice residual scattering, gating timing errors, and sensor noise make it difficult to separate target signals from the background. Consequently, the resulting images are generally affected by texture degradation and low contrast, severely limiting the accuracy of downstream tasks like object detection and environmental perception. To this end, we propose the use of a Wavelet-guided Multi-stage Gated Enhancement Network (WMGNet). Operating progressively across three stages, WMGNet’s first two stages employ an encoder–decoder architecture that leverages multi-scale frequency decomposition in the wavelet domain to pinpoint intra-slice scattering and decouple target signals from noise. To precisely extract fine details, we design a TextureBlock integrating feature gating (ConvGLU) and high-frequency attention (HFAttention). Additionally, a pixel-wise ground-truth guided attention module (GGAM) is introduced to optimize the precision and target-specificity of multi-stage feature fusion. Extensive comparative and ablation experiments demonstrate that the proposed WMGNet effectively eliminates scattering interference and restores texture details in underwater imaging. On our custom ULRGI dataset, it achieves state-of-the-art performance with a PSNR of 36.31 dB, an SSIM of 0.921, an MAE of 2.672, and an LPIPS of 0.060. Notably, it outperforms the second-best method by a margin of 3.06 dB in PSNR and reduces the MAE by 50.69%. Furthermore, evaluations on three public datasets confirm its robust cross-scenario generalization, yielding competitive PSNR values of 33.22 dB, 31.59 dB, and 32.06 dB, respectively. Overall, WMGNet provides a highly effective and robust solution for high-resolution underwater imaging. Full article

Accurate grade detection in graphite ore, which is a strategic and critical mineral resource, plays an important role in improving beneficiation efficiency and overall resource utilization. However, the scarcity of high-grade samples limits the performance of deep learning models in grade identification tasks. This limitation makes it difficult for models to learn stable and representative features. This paper proposes an enhanced CycleGAN-based image augmentation framework designed for graphite ore imagery. The method works within an unpaired image translation architecture. It introduces a distributed reflectance consistency loss. This loss encodes the graphite ore’s typical low reflectance and high optical contrast as explicit statistical constraints. The design enforces consistency in both the intensity distribution and the textural structure of the generated images. The model further integrates a convolutional block attention module into the generator. This module helps refine feature representation under a physics-inspired heuristic. The study constructs augmented training sets using the proposed method. It then evaluates these datasets with a downstream grade classification model. Experimental results show clear improvements. The method reduces Fréchet Inception Distance by 21.9% and Kernel Inception Distance by 39.4%. It also improves peak signal-to-noise ratio by 3.3% and structural similarity index measure by 2.6% compared with the baseline CycleGAN. The classification accuracy in the grade identification task increases by about 2.3 percentage points. These results show that the proposed method improves both the perceptual quality and the statistical consistency of synthetic graphite ore images. It also helps reduce the performance drop caused by limited training data in few-shot learning conditions. Full article

Visual perception is an important component of close-range maritime situational awareness, particularly when conventional sources such as AIS and radar are delayed, incomplete, or unavailable. Ship wakes provide motion-related visual cues, but their segmentation remains difficult because wake regions are elongated, weakly textured, and frequently mixed with water-surface clutter. This study develops Shipwake-YOLO, a wake-oriented adaptation of YOLOv9-Seg for ship and wake instance segmentation in inland-waterway images. The task is formulated as visual navigational-state cue extraction rather than validated future manoeuvre prediction. The model segments hull and wake instances and provides mask-derived spatial cues for possible downstream state interpretation. The architecture introduces iAFF into cross-scale feature fusion, adapts the high-level SPPELAN aggregation block with SAConv-enhanced convolution, replaces selected downsampling paths with iSACADown, and adopts MPD-IoU as the bounding-box regression loss. On a 2100-image dataset collected from the Wuhu Channel of the Yangtze River, Shipwake-YOLO improves Box-mAP@50 from 77.7% to 84.6% and Mask-mAP@50 from 67.6% to 79.8% relative to the YOLOv9-Seg baseline. Under stricter IoU thresholds, the model reaches 48.9 in Box-mAP@[0.50:0.95] and 46.2 in Mask-mAP@[0.50:0.95]. The parameter count is reduced by 7.5%, and GFLOPs decrease from 144.2 to 137.1. These results indicate that the proposed adaptation improves ship-wake perception within the collected inland-waterway setting and provides a visual basis for downstream navigational-state estimation. Full article

Accurate ripeness assessment of tomatoes in natural orchard settings is challenged by severe occlusion, dense clustering, and scale variation among fruits. This paper introduces YOLOv8n−TiD, a lightweight detection framework designed to overcome these obstacles. The architecture enhances the YOLOv8n backbone with a novel C2f-iRD module, which integrates re-parameterized dilated convolutions and inverted residual blocks to enlarge the receptive field while retaining fine-grained texture details. For feature fusion, we propose C2f-iRMB in the neck to ensure cross-scale consistency. To address semantic drift during upsampling, we replace standard interpolation with the DySample operator. The detection head is re-engineered as TADAH (Task-Adaptive Dynamic Alignment Head), enabling genuine task decoupling via deformable convolutions and conditionally shared features. Additionally, we introduce F−PIoUv2, a regression loss that emphasizes medium-quality predictions and curbs excessive bounding box expansion. Evaluations on a custom dataset show that YOLOv8n−TiD cuts parameters by 45%, FLOPs by 19%, and model size by 41%, while raising mAP@0.5 by 1.9 points—all in real time. On Android devices, it sustains 30 FPS inference and generalizes effectively to a distinct cherry tomato cultivar. These findings confirm the method’s robustness in discriminating occluded, small, and visually similar maturity stages, providing a practical vision system for robotic harvesting and field-based grading. Full article

Remote sensing image super-resolution plays an important role in object recognition, urban monitoring, and fine-grained remote sensing interpretation. This paper studies lightweight single-image remote sensing image super-resolution, in which only one LR observation is available and the model must recover reliable structural details under a limited computational budget. Existing lightweight methods reduce parameter counts and computational complexity, but their limited representation capacity often causes blurred boundaries, broken road structures, and missing high-frequency details in buildings, roads, and texture-rich regions. To address these issues, we propose SFEFeNet, a Structure-Frequency Mutual-Guided Lightweight Network for remote sensing image super-resolution. First, we design a Lightweight Structure-Frequency Block (LSFB) to jointly model local spatial features, structural responses, and frequency responses with low computational overhead. Second, we introduce a Structure-Frequency Mutual Guidance (SFMG) module, where edge responses guide high-frequency component selection, and the selected high-frequency responses further refine edge-aware attention. Finally, we propose a Structure-Frequency Fusion Gate (SFFG) to adaptively integrate lightweight features, local spatial features, frequency-enhanced features, and structure-refined features. Experiments on RSSCN7, DOTA, and WHU-RS19 datasets evaluate SFEFeNet in terms of reconstruction quality, visual performance, and model complexity. Additional analyses further examine structural preservation, complex synthetic degradation, real-image generalization, and statistical stability. Notably, SFEFeNet-Lite contains 0.539 M parameters and 17.07 G FLOPs for $\times 2$, and 0.622 M parameters and 7.12 G FLOPs for $\times 4$, enabling effective structure-frequency feature modeling with lightweight computational cost. Full article

Maritime search and rescue is an important component of emergency response frameworks and primarily relies on Unmanned Aerial Vehicles (UAVs) for maritime object detection. However, maritime accidents frequently occur in low-visibility environments, such as foggy or low-light conditions, which lead to low contrast, blurred object boundaries, and degraded texture representations. Most existing maritime object detection algorithms are developed for natural light scenes, and their performance deteriorates markedly when deployed directly in low-visibility environments, primarily due to reduced image quality that hinders feature extraction and semantic information aggregation. Although several studies incorporate image enhancement techniques prior to detection to improve image quality, these approaches often introduce significant additional computational overhead, limiting their practical deployment on UAV platforms. To tackle these challenges, this paper proposes a lightweight model built upon a recent YOLO framework, termed Multi-Scale Adaptive YOLO (MSA-YOLO), for maritime detection using UAVs in low-visibility environments. The proposed model systematically optimizes the backbone, neck, and detection head networks. Specifically, an improved StarNet backbone is designed by integrating Efficient Channel Attention (ECA) mechanisms and multi-scale convolutional kernels, which strengthen feature extraction capability while maintaining low computational overhead. In the neck network, a high-frequency enhanced residual block branch is inserted into the C3k2 module to capture richer detailed information, while depthwise separable convolution is utilized to further reduce computational cost. Moreover, a non-parametric attention module is incorporated into the detection head to adaptively optimize features in the classification and regression branches. Finally, a joint loss function that combines bounding box regression, classification, and distribution focal losses is utilized to improve detection accuracy and training stability. Experimental results on the constructed AFO, Zhoushan Island, and Shandong Province datasets demonstrate that, relative to YOLOv11-s, MSA-YOLO reduces model parameters and FLOPs by 52.07% and 41.36%, respectively, while achieving improvements of 1.11% and 1.33% in mAP@0.5:0.95 and mAP@0.5. These results indicate that the proposed method effectively balances computational efficiency and detection accuracy, rendering it suitable for practical maritime search and rescue applications in low-visibility environments. Full article

Façades play a decisive role in shaping the visual and symbolic character of historic urban environments. Recent European funding schemes promoting energy-efficient retrofitting have accelerated interventions on building envelopes. Although aligned with decarbonization objectives, these processes are generating significant chromatic and material transformations that risk eroding the visual coherence and cultural sustainability of consolidated urban areas. In the historic Ensanches of San Sebastián, the replacement of traditional envelope systems with new cladding solutions is leading to the loss of the architectural style of some facades and altering their materials, textures, and colors. A progressive “contagion effect” has been identified, whereby dissonant chromatic schemes—often associated with the proliferation of so-called “zebra blocks”, residential buildings with façades clad in alternating black and white stripes that have proliferated in recent urban developments—are replicated across adjacent buildings, gradually weakening spatial continuity and the genius loci of the neighborhood. In response to this phenomenon, this research develops a systematic methodology to analyze, quantify, and anticipate chromatic transformation in consolidated urban fabrics. The study combines historical morphological analysis, classification of architectural periods, and chromatic mapping of recent façade interventions. Based on this framework, a CARI, Chromatic Alteration Risk Index is proposed to evaluate the potential impact of façade alterations on urban chromatic coherence. Drawing on an epidemiological framework, the methodology enables the identification of critical transformation clusters, the assessment of contagion dynamics, and the definition of regulatory thresholds for color and material interventions. By integrating perceptual criteria, urban morphology, and spatial distribution patterns, the study moves beyond descriptive diagnosis and offers a transferable tool for municipal planning. The proposed approach supports the proactive regulation of façade rehabilitation processes, balancing energy efficiency objectives with the preservation of collective memory, material identity, and urban sensory quality. This study proposes a quantitative model of “urban chromatic contagion” to assess how façade color interventions propagate within a neighborhood. We define the Chromatic Integration Percentage (CIP) and the Chromatic Alteration Risk Index (CARI) of the analyzed area. Results indicate that poorly regulated façades show higher chromatic dissonance (low CIP) and act as contagion hotspots, while a clear risk gradient emerges: highly protected buildings present lower risk, whereas mixed typologies and recent rehabilitations concentrate higher CARI values. The model supports preventive urban color management by identifying areas at risk before visible alteration. Full article

Accurate and robust detection of leaf diseases is a key enabler for precision agriculture and large-scale crop health monitoring. Despite the strong generalization of modern one-stage detectors (e.g., YOLOv8), two domain-specific challenges remain: (i) weak or blurry lesion boundaries hinder precise localization, and (ii) low color contrast between diseased and healthy tissues forces models to rely on subtle texture patterns rather than salient shapes. To tackle these challenges, we reframe the core agricultural disease detection task as the identification of “asymmetric morphological anomalies” and propose a domain-tailored enhancement framework. First, we introduce an Edge Enhancement Module (EEM) that explicitly strengthens boundary-aware representations. Inspired by the natural symmetry of healthy leaves, our EEM is specifically designed to capture symmetry-breaking boundary discontinuities and localized asymmetric edges caused by disease lesions. Our method enhances edge and texture cues that are indicative of disease lesions, which often exhibit local asymmetries and boundary discontinuities. The EEM includes a Differential Normalized Pooling Block (DNPB) that highlights edge responses through discrepancies between max pooling and average pooling, which also models cross-group edge correlations. Second, the Lightweight Texture-Sensitive Feature Enhancement (LTSFE) mechanism amplifies texture-discriminative channels under low-contrast conditions by leveraging complementary global statistics and efficient channel mixing, all with negligible computational overhead. We evaluated our method on a self-constructed dataset of 106,434 images with 225,640 annotations covering diverse crops. Experiments show that the proposed method achieves state-of-the-art accuracy (81.54% mAP@0.5:0.95) while maintaining real-time inference (142 FPS), consistently outperforming strong baselines. Ablations confirm the effectiveness and complementarity of EEM and LTSFE, demonstrating that domain-specific architectural design, inspired by biological symmetry, can substantially improve agricultural vision systems. Full article

Small object detection in remote sensing imagery is a fundamental task for visual information extraction, yet it remains challenging due to extremely small target scales, complex backgrounds, and the loss of discriminative feature information caused by repeated downsampling. To address these issues, this paper proposes a Wavelet-Convolution and Fine-Grained Preservation Network (WCFPNet) based on YOLOv8n. Specifically, a Wavelet-Convolution Module (WCM) is introduced into the backbone to decompose feature maps into low- and high-frequency sub-bands, thereby enhancing structural feature modeling and preserving subtle target details. To compensate for the weakened fine-grained information after repeated downsampling, an Enhanced Spatial Pyramid Pooling-Fast (ESPPF) module is embedded at the end of the backbone to strengthen multi-scale contextual aggregation. In addition, an Enhanced Feature Pyramid Network (EFPN) is designed in the neck to facilitate the propagation of shallow and intermediate fine-grained features to high-level semantic features through cross-level fusion and the Convolutional Block Attention Module (CBAM). Experiments on the NWPU VHR-10 dataset show that WCFPNet achieves 0.879 mAP@0.5 and 0.515 mAP@0.5:0.95, outperforming YOLOv8n by 1.7 and 2.5 percentage points, respectively. Moreover, the proposed WCFPNet achieves a competitive performance compared with several representative detectors while maintaining moderate model complexity. These results demonstrate the effectiveness of WCFPNet in challenging remote sensing scenes characterized by complex backgrounds, dense object distributions, and weak textures. Full article

Dehazing of images is important for proper interpretation of optical images in remote sensing. However, current dehazing networks tend to have limited receptive field and texture information loss caused by conventional downsampling and complementary cross-domain information not being utilized in dehazing frameworks. In order to cope with these problems, we propose a Cross-domain Generative Prior-assisted Structure–Texture Adaptive Network for remote sensing image dehazing. It is a dual-stream encoder–decoder framework, which enhances the domain-specific information of RGB and generated prior, and then integrates them adaptively for haze-free reconstruction. In order to minimize information loss in downsampling, wavelet pooling is introduced to consider the frequency-aware structural and textural features. Additionally, a Structure–Texture Calibration Block is designed to simultaneously improve the local frequency textures and construct sparse long-range dependencies of structures, so as to achieve better restoration performance under spatially non-uniform haze. To appropriately fuse the various representations from RGB and generated prior images, a Prior-aware Gated Adaptive Fusion module is developed to balance the domain-specific features dynamically and keep the fine details at multi-level feature fusion. Finally, we utilize pixel-level contrastive learning to guide the latent space away from hazy distributions, thus enhancing the discriminability of the features. Extensive experiments on the three datasets, namely RSID, RICE-I and HRSD, demonstrate that CGSTA-Net can effectively restore images under varying haze conditions and significantly outperforms the latest dehazing methods in terms of visual quality and quantitative performance. Specifically, compared with the most effective competitive method, CGSTA-Net increased the PSNR by 22.9% on RSID, by 13.2% on RICE-I, and by 7.2% on HRSD. Full article

Forests of Lualaba Province (DR Congo) form a compositionally complex mosaic of dry dense forest, gallery forest, and Miombo woodland. Yet, categorical land-cover maps impose discrete boundaries on these inherently continuous vegetation gradients, systematically discarding subpixel compositional information critical for forest monitoring and carbon accounting. The magnitude of this information loss at the landscape scale, however, remains largely unquantified. In this study, we train a Multi-Output Neural Network (MONN) using Sentinel-2 spectral and textural predictors (2025) to estimate the proportional cover of three forest types across the province. Model performance is benchmarked against a normalised Random Forest (RF) using spatial block cross-validation. Categorical information loss is quantified pixel-wise using two complementary metrics, dominant class proportion and Shannon compositional entropy, alongside a derived interpretive quantity, categorical information loss. The MONN slightly outperformed RF (R² = 0.648 vs. 0.630; RMSE = 0.224 vs. 0.229), yet the results reveal a fundamentally heterogeneous landscape structure. The mean dominant-class proportion was only 56.2%, indicating that categorical maps discard, on average, 43.8% of compositional information per pixel. Only 7.9% of forested pixels exceeded the 75% dominance threshold, while Shannon entropy reached 74.1% of its theoretical maximum, indicating that forest types coexist in near-equal proportions across most pixels. This renders categorical attribution structurally inadequate for most of the forested landscape. Across 92.1% of forested pixels, no single forest type achieved clear dominance. These results show that compositional mixing is the dominant structural condition of the landscape, and that compositional mapping is essential for representing tropical forest structure in heterogeneous drylands. By formally quantifying categorical information loss at the landscape scale, this study shows that continuous compositional mapping converts this structural ambiguity into a spatially explicit ecological signal, with direct implications for monitoring vegetation dynamics and biodiversity, suggesting a structural source of error in carbon stock estimation in tropical dry forests that warrants empirical validation. Full article

Image dehazing is a fundamental visual restoration task for improving visual perception under low-visibility weather conditions, especially in UAV-based remote sensing, traffic monitoring, and surveillance scenarios. Existing convolutional neural networks are effective in local feature extraction but remain limited in long-range dependency modeling, while Transformer-based methods improve global modeling at the cost of high computational complexity. To address these issues, this paper proposes an efficient image-dehazing framework termed FSSM, which integrates frequency-enhanced State Space Modeling with a hierarchical encoder–decoder architecture. Specifically, an FFT-based State Space Block (FFTSSB) is designed to reformulate state propagation as frequency-domain two-sided non-causal convolution, enabling efficient bidirectional global dependency modeling without explicit recursive scanning. Furthermore, a Frequency-Aware Discriminative Enhancement Block (FDEB) is introduced to enhance local textures, edges, and structural details through spatial gating and lightweight block-wise frequency modulation. Based on these two components, a Frequency-Aware State Interaction (FASI) block is constructed to progressively couple global state propagation and local frequency-aware enhancement. Experimental results on the HazyDet dataset demonstrate that FSSM achieves favorable restoration accuracy, structural consistency, and perceptual quality compared with representative dehazing methods. Ablation studies further validate the effectiveness of the proposed two-sided FFT-based state modeling, frequency-aware enhancement, and hierarchical multi-scale design. Full article

Accurate surface defect classification is a critical requirement in structural health monitoring and infrastructure inspection, where defects, including cracks, spalling, delamination and noncrack regions, often appear with low-contrast and complex background textures. Motivated by the need for a robust and discriminative framework that can enhance defect visibility and focus learning on damage-critical regions, this research proposes a novel damage-aware DenseNet-201 (DA-DenseNet-201) model for surface defect classification. As a critical novelty, a damage-aware adaptive contrast-limited adaptive histogram equalisation (DAC) filtering strategy is introduced as a preprocessing stage. The proposed DAC filter dynamically adjusts contrast enhancement parameters based on damage indicators, selectively amplifying crack edges and defect textures while preserving healthy surface regions and suppressing noise. Building on this method, enhanced images are processed using a pretrained DenseNet-201 backbone, retaining the benefits of dense feature propagation and efficient gradient flow. To strengthen the discriminative learning of DA-DenseNet-201 further, an attention refinement block is integrated into the network, combining channel attention to emphasise defect-relevant feature responses and spatial attention to localise damage regions accurately. In addition, a multiscale feature fusion mechanism aggregates feature maps from multiple dense blocks to capture fine-grained crack patterns, texture-level degradation and high-level semantic damage information. Extensive experiments conducted on surface defect datasets demonstrate its effectiveness, achieving a superior classification accuracy of 98.93%, along with notable improvements in sensitivity, specificity and the intersection over union compared with state-of-the-art models. These results confirm that the proposed DA-DenseNet-201 provides a reliable and high-performance solution for automated surface defect classification. Full article