Search Results (4,493)

Aimed at the practical challenge that pest occurrence in fruit and vegetable horticultural production exhibits strong environmental dependency, pronounced stage characteristics, and high sensitivity to control decision-making, a multimodal pest recognition and occurrence risk joint modeling method is proposed to address the limitation that conventional intelligent plant protection systems focus primarily on pest identification while lacking risk discrimination capability. Within a unified network framework, pest visual information and environmental temporal data are integrated through the construction of an environment-guided representation learning mechanism, a recognition–risk joint optimization strategy, and a risk-aware decision representation modeling structure. In this manner, pest category recognition and occurrence risk evaluation are conducted simultaneously, thereby providing direct decision support for precision prevention and control in fruit and vegetable production. Systematic experimental evaluation is conducted based on multi-crop and multi-year field data collected from Wuyuan County, Bayannur City, Inner Mongolia. Overall comparative results demonstrate that an identification accuracy of $0.947$, a precision of $0.936$, and a recall of $0.924$ are achieved on the test set, all of which significantly outperform mainstream visual detection models such as YOLOv8, DETR, and Mask R-CNN. In terms of detection performance, mAP@50 and mAP@75 reach $0.962$ and $0.821$, respectively, indicating stable localization and discrimination capability under complex backgrounds and dense small-target conditions. For the occurrence risk discrimination task, a risk accuracy of $0.887$ is obtained, representing an improvement of approximately $4.5$ percentage points compared with the simple multimodal feature concatenation method. Cross-crop, cross-site, and cross-year generalization experiments further show that risk accuracy remains above $0.84$ with stable recognition performance under significant distribution shifts. Ablation studies verify the synergistic contributions of the proposed core modules to overall performance improvement. The results indicate that the proposed framework enables the transition from single recognition to risk-driven plant protection decision-making, providing a technically viable pathway for pest diagnosis and control strategy optimization in fruit and vegetable horticulture. Full article

As urbanization transitions from incremental expansion to the optimized utilization of existing construction land, the precise identification of land-use status and changes has become a core requirement for enhancing refined land resource management. However, in urban built environments characterized by dense object distributions and complex geometric contours, existing change detection methods often struggle to capture subtle boundaries, leading to edge blurring and loss of detail. To address these challenges, this study proposes an Edge-aware Change Detection Network for urban construction land change identification. The model features a shared Siamese encoding network based on MiT-B1, leveraging its hierarchical multi-scale attention mechanism to balance local detail extraction with long-range semantic dependency capture, thereby overcoming the limitations of monolithic feature extraction. Furthermore, a multi-level feature concatenation and fusion strategy is designed to align and interact with bi-temporal features along the channel dimension, significantly enhancing the saliency and discriminative representation of change areas. Experimental results on the Yongzhou building change detection dataset demonstrate that the proposed model outperforms state-of-the-art methods in both visual recognition and quantitative metrics. It effectively resolves the difficulty of boundary definition in complex urban scenarios, providing localized high-precision technical support for the assessment and dynamic monitoring of construction land within the study area. Full article

Smart-city IoT ecosystems depend on a large number of devices with limited resources, which often lack built-in security mechanisms. While traditional cloud-based or gateway-centric intrusion detection systems (IDSs) offer essential security, they are still characterized by high detection latency, considerable bandwidth demand, and a lack of precise monitoring of single device actions. This study proposes the Edge AI Bridge, a novel micro-computing security layer positioned between IoT devices and the gateway to enable early-stage threat interception. The architecture integrates embedded AI hardware with a hybrid pipeline, utilizing unsupervised anomaly detection for behavioral profiling and a lightweight signature-matching module to minimize false positives. System operations—including localized traffic inspection, protocol parsing, and feature extraction—are performed before data aggregation, which preserves device-level privacy and reduces the computational burden on the IoT gateway. The contemporary CIC-IoT-2023 dataset, which captures a wide range of smart-city protocols and attack vectors, is used to evaluate the architecture. The Edge AI Bridge leads to a significant reduction in detection latency—≈50 ms on average as opposed to the 500 ms of cloud-based solutions—while the resource footprint is kept low to about 20% CPU utilization. The Edge AI Bridge demonstrates a potential solution that is scalable, modular, and can preserve privacy while improving the cyber resilience of the smart-city infrastructures that are large, heterogeneous, and difficult to manage. Full article

This paper presents a non-contact depth detection method for corrugated heat exchanger plates, aiming to improve measurement efficiency and accuracy. The system integrates a line laser sensor with a precision linear guide rail, enabling continuous acquisition of high-resolution 2D surface profiles as the sensor moves along the plate. To reduce data redundancy while preserving geometric features, a multi-stage data reduction strategy is proposed. This strategy combines the angle–chord height criterion with spline-based filtering to identify key regions of curvature and eliminate unnecessary point cloud data. For depth extraction, a two-stage feature recognition algorithm is designed. First, a coarse analysis locates candidate peaks and valleys by identifying local extrema in the reduced 2D data. Then, a fine detection process is applied: local B-spline fitting is performed near each candidate point, and a binary search algorithm is used to accurately determine the spline extrema. By computing the vertical distance between precisely located peaks and valleys, the system rapidly extracts the corrugation depth parameters. This method achieves a high balance between speed and precision, offering a practical and reliable solution for automated surface morphology inspection in heat exchanger manufacturing. Full article

Complex farming environments, breed variation, and the high cost of manual annotation remain major obstacles to robust pig detection, while cross-breed detection under few-shot conditions has been insufficiently explored in previous studies. To address this gap, we propose a few-shot pig detection framework that combines an improved YOLOv7 detector with CycleGAN-based pseudo-sample generation. The detector was enhanced through anchor optimization, Efficient Channel Attention (ECA), and Log-Sum-Exp (LSE) pooling to improve localization and feature discrimination in dense pigsty scenes. In addition, an optimized CycleGAN with perceptual loss was used to generate synthetic Duroc-like pig images to enrich the limited target-domain training set. The framework was evaluated using a two-dataset design: a White Pig Base Dataset was used to establish the source-domain detector and validate the architectural improvements, whereas a Duroc Pig Few-Shot Dataset was used to assess cross-breed adaptation under a 10-shot setting. The experimental results show that the proposed method achieved 98.16% mAP on the White pig dataset and 85.52% mAP on the Duroc Few-Shot Dataset. On the Duroc Few-Shot Dataset, the final framework outperformed Faster R-CNN, CenterNet, and YOLOv8, and also surpassed DCGAN- and SRGAN-based augmentation strategies. These results indicate that the proposed method provides an effective and practical solution for cross-breed few-shot pig detection, with potential value for intelligent livestock monitoring under annotation-limited conditions. Full article

The automatic extraction of seismic reflection events is fundamental to seismic interpretation and structural identification in oil and gas exploration, particularly for large-scale regional surveys and preliminary basin-scale assessments. Although the B-COSFIRE (Bar-Combination of Shifted Filter Responses) method has demonstrated strong capability in detecting ridge-like structures, its application in large-scale seismic processing is limited by high computational cost and complex filter bank configuration. Conventional edge detectors such as the Canny operator are computationally efficient but often produce fragmented and noise-sensitive results in low signal-to-noise ratio (SNR) seismic data because they rely solely on local gradient information and ignore the spatial continuity of geological horizons. To overcome these limitations, this study proposes a lightweight and computationally efficient framework for rapid seismic event extraction. The method simplifies the B-COSFIRE architecture by replacing its configurable filter bank with a Difference-of-Gaussian (DoG) operator, which enhances ridge-like reflection features while suppressing background interference through a center–surround mechanism. Furthermore, a Spatial Consistency Constraint (SCC) module is introduced to enforce lateral continuity using directional morphological closing operations. This strategy reconstructs disrupted reflection segments and converts isolated detection responses into spatially coherent linear structures. Adaptive thresholding and skeletonization are then applied to obtain single-pixel-wide reflection contours suitable for geological interpretation and regional structural analysis. The proposed method was evaluated using both synthetic seismic models (Ricker wavelet convolution with Gaussian noise, σ = 0.15) and real post-stack seismic profiles characterized by low SNR conditions. Experimental results demonstrate that the proposed method achieves a Precision of 0.9527, Recall of 1.0000, and F1-score of 0.9758 on synthetic data, outperforming both the standard Canny detector (F1: 0.8972) and B-COSFIRE (F1: 0.7311). The Continuity Index reaches 261.00 pixels, substantially higher than Canny (223.67 pixels) and B-COSFIRE (66.86 pixels). Notably, B-COSFIRE exhibits a severely imbalanced detection profile (Precision: 0.5762, Recall: 1.000), indicating excessive false positives that undermine its practical utility. The proposed method additionally achieves the lowest runtime (0.024 s per profile), representing a 44× speedup over B-COSFIRE (1.039 s), while requiring no training data. Overall, the proposed framework provides a practical and efficient solution for automated seismic event extraction. With only a small number of geologically interpretable parameters and strong robustness across different datasets, the method is well-suited for large-scale seismic data processing and preliminary structural assessment in underexplored regions, enabling rapid first-pass evaluation of extensive survey areas before detailed interpretation and reservoir characterization. These characteristics make the method particularly suitable for computer-assisted interpretation workflows in industrial oil and gas exploration. Unlike prior approaches that treat seismic event extraction as a generic edge detection problem, the proposed framework explicitly encodes geological prior knowledge—specifically, the lateral continuity of stratigraphic interfaces—as a morphological constraint, bridging the gap between image processing methodology and geophysical interpretation requirements. Full article

Timely fault detection, classification, and localization are fundamental to enabling fast service restoration in modern distribution networks, and are especially vital for maintaining the reliability and resilience of smart city electricity infrastructures. A new AI-based method for classifying and localizing fault types is presented in this paper, which enhances situational awareness in smart distribution grids that supply dense urban loads and critical smart city services. The proposed approach targets various fault conditions, which include three-phase-to-ground, three-phase, two-phase-to-ground, two-phase, and single-phase-to-ground faults. The proposed method utilizes a wavelet-based signal processing technique to analyze the feeder’s current data captured by waveform measurement units (WMUs) and extracts features for fault analysis. As a result of these features, a multi-stage machine learning architecture incorporating deep learning components is developed to accurately determine the occurrence, type, and location of faults. To evaluate the performance of the proposed approach, simulations were conducted on a 16-bus distribution network. Results show a high level of accuracy in fault detection, classification, and localization. This indicates that the method can be a valuable tool for enhancing the resilience and intelligence of future power grids, as well as supporting self-healing and fast service restoration in smart city services. Full article

Hybrid multi-backbone architectures and the utilization of edge cues for auxiliary training have become two major research trends in salient object detection (SOD). It is widely acknowledged that CNNs can effectively model local spatial structures, while Transformers can capture long-range global dependencies. However, the representation discrepancy between CNN and Transformer features, together with boundary-detail degradation during multi-scale fusion, remains a major challenge. In addition, how to effectively leverage edge cues as reliable structural guidance without introducing texture-induced false boundaries or boundary leakages remains an open issue. In this paper, we present SECA-Net, a unified framework that establishes a profound synergy between CNN and Transformer representations. It explicitly bridges their inherent discrepancies through level-dependent interaction strategies, while resolving structural degradation via a sequential “purify-and-guide” mechanism. This approach enables the network to extract and utilize edge cues effectively, thereby alleviating boundary degradation and texture-induced false contours. Specifically, we design a dual-encoder structure to extract features. A level-wise feature interaction (LFI) module is introduced to perform discrepancy-aware fusion across feature levels, stabilizing CNN–Transformer aggregation. Meanwhile, the features extracted from the CNN branch are projected into a semantic-aware edge refinement (SAER) module to produce clean multi-scale edge priors under high-level semantic guidance, suppressing texture-induced spurious edges. Finally, we design an edge-guided cross-attention feature aggregation (ECFA) module, which progressively injects refined edge priors as structural constraints into multi-scale saliency decoding via cascaded cross-attention, enabling effective structural refinement. Overall, LFI reduces cross-branch discrepancy, SAER purifies boundary priors, and ECFA integrates semantics and structure in a progressive decoding manner, forming a unified SECA-Net framework. Extensive experimental results on five benchmark SOD datasets show that SECA-Net outperforms 19 state-of-the-art methods, demonstrating its effectiveness. Specifically, our proposed method ranks first in $F_{\beta }$ and $BDE$ across all datasets, notably improving $F_{\beta }$ by $1.54\%$ on the challenging DUTS-TE dataset. Full article

Printed circuit board (PCB) defect inspection is critical for ensuring product reliability, yet it remains challenging due to the microscopic scale of defects and complex background patterns. To improve the localization of fine anomalies, this paper proposes EAS-DETR, an efficient and highly sensitive real-time end-to-end detector. First, we reconstruct the feature extraction backbone by introducing a novel C2f-EC module, which jointly models local textures and global structural dependencies. Second, an Adaptive Sparse Attention-based Intra-scale Feature Interaction (ASAFI) module is proposed to suppress background noise and focus the network’s attention on sparse defect regions. Finally, an optimized feature pyramid network, SGO-FPN, is designed to mitigate cross-scale feature misalignment and preserve high-resolution spatial details for small object localization. Experiments demonstrate that EAS-DETR achieves an mAP@0.5 of 93.0% and a 91.9% recall on a multi-source PCB dataset. The model outperforms mainstream YOLO variants and baseline RT-DETR models while maintaining a moderate parameter count of 14.6M and achieving a real-time inference speed of over 70 FPS. Furthermore, cross-domain validations on public benchmarks confirm its robust generalization capability for complex tiny object detection tasks. Full article

Composite materials are critical components in advanced equipment such as aerospace, however, delamination defects that readily arise during manufacturing and in service present serious risks to equipment safety. Terahertz non-destructive testing is highly effective for analyzing the internal structure of composite materials, making it an effective approach for precise identification of delamination defects. Current terahertz detection approaches mainly depend on single domain features, making it difficult to capture complementary information from both the time and frequency domains. To address this, a Time-Frequency Feature-fusion Network (TFFN) is proposed. In this network, a three-branch architecture is employed: local transient patterns and pulse-related structural features are extracted by the local time-frequency branch; damage-sensitive frequency bands are focused on by the frequency-domain branch through a channel-space-frequency band attention mechanism; and deep integration of time-frequency features is achieved by the time-frequency fusion branch using Manifold Mixup. Finally, the features extracted from the three branches are adaptively fused via a cross-branch attention mechanism, and defect identification is accomplished by the classifier. Experimental results show that this method achieves accuracies of 98.40% on the glass fiber reinforced polymer (GFRP) dataset and 98.63% on the quartz fiber reinforced polymer (QFRP) dataset, surpassing the best existing method by 2% and 1.25%, respectively. A substantial improvement in both defect identification accuracy and the model’s generalization ability for layered structures is thereby achieved. Full article

While vision-language models (VLMs) have been widely applied in zero-shot anomaly detection (ZSAD), their performance remains limited by the inability to distinguish fine-grained normal and abnormal textures, coupled with inadequate capabilities in detecting complex morphological anomalies. To address these limitations, this paper proposes BAG-CLIP (Bifurcated Attention Graph-Enhanced CLIP), a dual-path graph-enhanced zero-shot anomaly detection method. This approach employs a Bifurcated Self-Attention (BSA) module to decouple visual features, processing global semantics and spatial details separately to mitigate the inherent conflict between abstract semantic representation and precise spatial localization. A Self-Attention Graph (SAG) module is designed to model the topological structure of complex morphological anomalies. This module dynamically constructs visual features’ topological relationships and utilizes graph convolutions to aggregate neighborhood information, thereby enhancing the model’s representational capacity for diverse and complex morphological anomalies. Extensive experiments are conducted on five diverse industrial datasets, featuring complex transmission line backgrounds alongside general industrial scenarios. The proposed method is comprehensively evaluated against 11 state-of-the-art (SOTA) methods. On the EPED (Electrical Power Equipment Dataset) and MPDD datasets, BAG-CLIP outperforms the second-best methods in image-level AUROC (Area Under the Receiver Operating Characteristic Curve) by 3.7% and 2.8%, respectively. BAG-CLIP achieves superior performance in both zero-shot anomaly detection and segmentation. Full article

Precise segmentation of transmission equipment is crucial for ensuring secure power grid operation, yet practical deployment faces substantial challenges including the preservation of elongated morphological characteristics of transmission lines and accurate boundary localization for complex transmission tower structures. This paper proposes a novel segmentation method that synergistically integrates cross-directional convolutions with multi-layer attention mechanisms within the YOLO11 framework. The designed C3x cross-directional convolution module incorporates orthogonal convolutional operations during feature extraction, enabling independent enhancement of feature responses along horizontal and vertical dimensions. This architecture effectively captures continuous morphological characteristics of elongated targets while mitigating fragmentation artifacts. Additionally, the proposed Multi-Layer Cascaded Attention (MLCA) module employs a progressive fusion strategy combining spatial and channel attention, significantly augmenting the network’s capacity to extract multi-scale semantic information while maintaining computational efficiency. This design particularly enhances boundary detail preservation for structurally complex targets. Experimental evaluations on the TTPLA dataset (comprising 1232 images across 4 categories) demonstrate remarkable performance improvements: bounding box detection achieves 72.56% mAP@0.5 and mask segmentation reaches 68.37% mAP@0.5, representing gains of 2.97% and 4.52% respectively over the baseline YOLO11 model. The Mask F1 score improves from 67.85% to 71.76%, comprehensively validating the proposed method’s effectiveness in enhancing segmentation capabilities for both elongated and morphologically complex targets. These results substantiate the practical applicability of the proposed approach for intelligent transmission infrastructure monitoring systems. Full article

IQ67-domain (IQD) proteins are plant-specific calmodulin (CaM)/calmodulin-like (CML) targets implicated in the spatial organization of Ca²⁺ signaling, yet their roles in tartary buckwheat (Fagopyrum tataricum) remain largely unexplored. Here, we identified 24 FtIQD genes and classified them into six phylogenetic subfamilies. FtIQDs show uneven chromosomal distribution and mainly arise from segmental duplication under purifying selection. Promoter analysis revealed the enrichment of MYB-, light-, and ABA-related cis-elements. To link FtIQDs with rutin variation, we performed an FtIQD-focused association analysis using whole-genome resequencing data from altitude-stratified panels of up to 220 accessions. Under additive, dominant, and recessive models, multiple significant SNPs (p < 1 × 10⁻⁵) were detected near a subset of FtIQD loci, showing clear model- and environment-dependent patterns. Recurrent loci included FtIQD22, FtIQD02, FtIQD16, and FtIQD19. RNA-seq under PEG-induced drought stress, tissue expression patterns, pathway co-expression, and qRT–PCR further prioritized FtIQD19. FtIQD19–GFP showed predominant nuclear localization with additional filamentous/peripheral signals, and yeast two-hybrid assays identified FtCaM7.2 as the strongest interactor among representative CaMs. Structural modeling of the FtIQD19–FtCaM7.2 complex suggested testable residue-level interaction features. Collectively, this work provides a foundational FtIQD resource and highlights candidate Ca²⁺/CaM–IQD modules potentially associated with altitude-dependent rutin variation in tartary buckwheat. Full article

Small object detection in UAV imagery presents challenges due to factors such as minute scale, indistinct features, and severe background clutter, which constrain the recognition performance of end-to-end models like RT-DETR. To enhance detection accuracy for small-scale objects, this paper proposes MSA-DETR, a Multi-scale Spatial Attention-enhanced detection model based on RT-DETR (Res18). Three specific structural improvements are introduced. First, a PercepConv module is designed to capture comprehensive multi-scale information through 1 × 1, 3 × 3, and 5 × 5 convolutions, as well as dilated convolutions. This module integrates a lightweight channel attention mechanism to adaptively emphasize regions containing small objects. Second, the SODAttention module is introduced to jointly model local spatial details and global contextual information, thereby enhancing the discriminative capability in key regions and significantly suppressing interference from complex backgrounds. Finally, a dedicated small object detection layer is added to the detection head, incorporating shallow fine-grained features to compensate for the semantic limitations of deep layers concerning small targets. Experimental results demonstrate that the proposed MSA-DETR achieves significant performance gains on the VisDrone2019 dataset, increasing mAP@50 from 47.5% to 52.2% and mAP@50–95 from 29.3% to 33.2%. Moreover, the proposed model outperforms the baseline by an absolute margin of 1.9% on the small-object-specific metric APs, achieving 20.3%. These results validate the effectiveness of the proposed method for small object detection in UAV scenarios. Full article

In unmanned aerial vehicle (UAV)-based monocular inspection, cracks typically present as geometrically asymmetric, elongated, low-contrast weak targets, making accurate segmentation and spatial localization challenging. Existing methods are susceptible to missed detections and false positives when handling slender cracks, and monocular 3D reconstruction for localization is often burdened by redundant frames, resulting in limited modeling efficiency. To mitigate these issues, we propose a high-precision framework for crack segmentation and spatial localization from UAV imagery. First, Oriented FAST and Rotated BRIEF–Simultaneous Localization and Mapping, version 3 (ORB-SLAM3) is adopted for keyframe selection to suppress data redundancy and improve reconstruction stability. Second, we develop an enhanced YOLOv11-seg model by integrating the Dilation-wise Residual Segmentation (DWRSeg) module, the Weighted IoU (WIoU) loss, and the Lightweight shared convolutional separator batch-normalization detection head (LSCSBD) to strengthen feature discrimination and segmentation robustness for slender cracks, yielding high-quality crack masks. Finally, the predicted masks are projected onto the reconstructed 3D surface to obtain precise spatial localization. Our experimental results demonstrate that the proposed approach improves the segmentation mAP@50 by 7.2% over the baseline while reducing computational complexity from 10.2 to 9.8 GFLOPs. In addition, keyframe-based processing reduces the 3D modeling time by 59.4% compared to that with full-frame reconstruction. Overall, the proposed framework jointly enhances crack segmentation accuracy and substantially accelerates 3D modeling and localization, providing an effective solution for efficient UAV-based crack inspection. Full article