Search Results (85)

Digital image quality is crucial for reliable analysis in applications such as medical imaging, satellite remote sensing, and video surveillance. However, traditional denoising methods struggle to balance noise removal with detail preservation and lack adaptability to various types of noise. We propose a novel three-module architecture for image denoising, comprising a generator, a dual-path-UNet-based denoiser, and a discriminator. The generator creates synthetic noise patterns to augment training data, while the dual-path-UNet denoiser uses multiple receptive field modules to preserve fine details and dense feature fusion to maintain global structural integrity. The discriminator provides adversarial feedback to enhance denoising performance. This dual-path adversarial training mechanism addresses the limitations of traditional methods by simultaneously capturing both local details and global structures. Experiments on the SIDD, DND, and PolyU datasets demonstrate superior performance. We compare our architecture with the latest state-of-the-art GAN variants through comprehensive qualitative and quantitative evaluations. These results confirm the effectiveness of noise removal with minimal loss of critical image details. The proposed architecture enhances image denoising capabilities in complex noise scenarios, providing a robust solution for applications that require high image fidelity. By enhancing adaptability to various types of noise while maintaining structural integrity, this method provides a versatile tool for image processing tasks that require preserving detail. Full article

Infrared small-target detection remains a critical challenge in military reconnaissance, environmental monitoring, forest-fire prevention, and search-and-rescue operations, owing to the targets’ extremely small size, sparse texture, low signal-to-noise ratio, and complex background interference. Traditional convolutional neural networks (CNNs) struggle to detect such weak, low-contrast objects due to their limited receptive fields and insufficient feature extraction capabilities. To overcome these limitations, we propose a Multi-Scale Edge-Aware Convolution (MEAC) module that enhances feature representation for small infrared targets without increasing parameter count or computational cost. Specifically, MEAC fuses (1) original local features, (2) multi-scale context captured via dilated convolutions, and (3) high-contrast edge cues derived from differential Gaussian filters. After fusing these branches, channel and spatial attention mechanisms are applied to adaptively emphasize critical regions, further improving feature discrimination. The MEAC module is fully compatible with standard convolutional layers and can be seamlessly embedded into various network architectures. Extensive experiments on three public infrared small-target datasets (SIRSTD-UAVB, IRSTDv1, and IRSTD-1K) demonstrate that networks augmented with MEAC significantly outperform baseline models using standard convolutions. When compared to eleven mainstream convolution modules (ACmix, AKConv, DRConv, DSConv, LSKConv, MixConv, PConv, ODConv, GConv, and Involution), our method consistently achieves the highest detection accuracy and robustness. Experiments conducted across multiple versions, including YOLOv10, YOLOv11, and YOLOv12, as well as various network levels, demonstrate that the MEAC module achieves stable improvements in performance metrics while slightly increasing computational and parameter complexity. These results validate the MEAC module’s significant advantages in enhancing the detection of small and weak objects and suppressing interference from complex backgrounds. These results validate MEAC’s effectiveness in enhancing weak small-target detection and suppressing complex background noise, highlighting its strong generalization ability and practical application potential. Full article

The underground coal mine environment is complex and dynamic, making the application of visual algorithms for object detection a crucial component of underground safety management as well as a key factor in ensuring the safe operation of workers. We look at this in the context of helmet-wearing detection in underground mines, where over 25% of the targets are small objects. To address challenges such as the lack of effective samples for unworn helmets, significant background interference, and the difficulty of detecting small helmet targets, this paper proposes a novel underground helmet-wearing detection algorithm that combines dynamic background awareness with a limited number of valid samples to improve accuracy for underground workers. The algorithm begins by analyzing the distribution of visual surveillance data and spatial biases in underground environments. By using data augmentation techniques, it then effectively expands the number of training samples by introducing positive and negative samples for helmet-wearing detection from ordinary scenes. Thereafter, based on YOLOv10, the algorithm incorporates a background awareness module with region masks to reduce the adverse effects of complex underground backgrounds on helmet-wearing detection. Specifically, it adds a convolution and attention fusion module in the detection head to enhance the model’s perception of small helmet-wearing objects by enlarging the detection receptive field. By analyzing the aspect ratio distribution of helmet wearing data, the algorithm improves the aspect ratio constraints in the loss function, further enhancing detection accuracy. Consequently, it achieves precise detection of helmet-wearing in underground coal mines. Experimental results demonstrate that the proposed algorithm can detect small helmet-wearing objects in complex underground scenes, with a 14% reduction in background false detection rates, and thereby achieving accuracy, recall, and average precision rates of 94.4%, 89%, and 95.4%, respectively. Compared to other mainstream object detection algorithms, the proposed algorithm shows improvements in detection accuracy of 6.7%, 5.1%, and 11.8% over YOLOv9, YOLOv10, and RT-DETR, respectively. The algorithm proposed in this paper can be applied to real-time helmet-wearing detection in underground coal mine scenes, providing safety alerts for standardized worker operations and enhancing the level of underground security intelligence. Full article

With the rapid development of deep learning, object detection in remote sensing images has attracted extensive attention. However, remote sensing images typically exhibit the following characteristics: significant variations in object scales, dense small targets, and complex backgrounds. To address these challenges, a novel object detection method named MCRS-YOLO is innovatively proposed. Firstly, a Multi-Branch Aggregation (MBA) network is designed to enhance information flow and mitigate challenges caused by insufficient object feature representation. Secondly, we construct a Multi-scale Feature Refinement and Fusion Pyramid Network (MFRFPN) to effectively integrate spatially multi-scale features, thereby augmenting the semantic information of feature maps. Thirdly, a Large Depth-wise Separable Kernel (LDSK) module is proposed to comprehensively capture contextual information while achieving an enlarged effective receptive field. Finally, the Normalized Wasserstein Distance (NWD) is introduced into hybrid loss training to emphasize small object features and suppress background interference. The efficacy and superiority of MCRS-YOLO are rigorously validated through extensive experiments on two publicly available datasets: NWPU VHR-10 and VEDAI. Compared with the baseline YOLOv11, the proposed method demonstrates improvements of 4.0% and 6.7% in mean Average Precision (mAP), which provides an efficient and accurate solution for object detection in remote sensing images. Full article

As the most basic mechanical components, bearing troubleshooting is essential to ensure the safe and reliable operation of rotating machinery. Bearing fault diagnosis is challenging due to the scarcity of bearing fault diagnosis samples and the susceptibility of fault signals to external noise. To address these issues, a ResNet-CACNN-BiGRU-SDPA bearing fault diagnosis method based on time–frequency bi-domain and feature fusion is proposed. First, the model takes the augmented time-domain signals as inputs and reconstructs them into frequency-domain signals using FFT, which gives the signals a bi-directional time–frequency domain receptive field. Second, the long sequence time-domain signal is processed by a ResNet residual block structure, and a CACNN method is proposed to realize local feature extraction of the frequency-domain signal. Then, the extracted time–frequency domain long sequence features are fed into a two-layer BiGRU for bidirectional deep global feature mining. Finally, the long-range feature dependencies are dynamically captured by SDPA, while the global dual-domain features are spliced and passed into Softmax to obtain the model output. In order to verify the model performance, experiments were carried out on the CWRU and JNU bearing datasets, and the results showed that the method had high accuracy under both small sample size and noise perturbation conditions, which verified the model’s good fault-feature-learning capability and noise immunity performance. Full article

The geolocalization of unmanned aerial vehicles (UAVs) in satellite-denied environments has emerged as a key research focus. Recent advancements in this area have been largely driven by learning-based frameworks that utilize convolutional neural networks (CNNs) and Transformers. However, both CNNs and Transformers face challenges in capturing global feature dependencies due to their restricted receptive fields. Inspired by state-space models (SSMs), which have demonstrated efficacy in modeling long sequences, we propose a pure Mamba-based method called the Cross-Mamba Interaction Network (CMIN) for UAV geolocalization. CMIN consists of three key components: feature extraction, information interaction, and feature fusion. It leverages Mamba’s strengths in global information modeling to effectively capture feature correlations between UAV and satellite images over a larger receptive field. For feature extraction, we design a Siamese Feature Extraction Module (SFEM) based on two basic vision Mamba blocks, enabling the model to capture the correlation between UAV and satellite image features. In terms of information interaction, we introduce a Local Cross-Attention Module (LCAM) to fuse cross-Mamba features, providing a solution for feature matching via deep learning. By aggregating features from various layers of SFEMs, we generate heatmaps for the satellite image that help determine the UAV’s geographical coordinates. Additionally, we propose a Center Masking strategy for data augmentation, which promotes the model’s ability to learn richer contextual information from UAV images. Experimental results on benchmark datasets show that our method achieves state-of-the-art performance. Ablation studies further validate the effectiveness of each component of CMIN. Full article

Vehicle detection algorithms constitute a fundamental pillar in intelligent driving systems and smart transportation infrastructure. Nevertheless, the inherent complexity and dynamic variability of traffic scenarios present substantial technical barriers to robust vehicle detection. While visual transformer-based detection architectures have demonstrated performance breakthroughs through enhanced perceptual capabilities, establishing themselves as the dominant paradigm in this domain, their practical implementation faces critical challenges due to the quadratic computational complexity inherent in the self-attention mechanism, which imposes prohibitive computational overhead. To address these limitations, this study introduces Mamba RT-DETR (MRD), an optimized architecture featuring three principal innovations: (1) We devise an efficient vehicle detection Mamba (EVDMamba) network that strategically integrates a linear-complexity state space model (SSM) to substantially mitigate computational overhead while preserving feature extraction efficacy. (2) To counteract the constrained receptive fields and suboptimal spatial localization associated with conventional SSM sequence modeling, we implement a multi-branch collaborative learning framework that synergistically optimizes channel dimension processing, thereby augmenting the model’s capacity to capture critical spatial dependencies. (3) Comprehensive evaluations on the BDD100K benchmark demonstrate that MRD architecture achieves a 3.1% enhancement in mean average precision (mAP) relative to state-of-the-art RT-DETR variants, while concurrently reducing parameter count by 55.7%—a dual optimization of accuracy and efficiency. Full article

Due to the inherent limitations of detection networks and the imbalance in training data, small-target detection has always been a challenging issue in the field of target detection. To address the issues of false positives and missed detections in small-target detection scenarios, a new algorithm based on STDA-YOLOv8 is proposed for small-target detection. A novel network architecture for small-target detection is designed, incorporating a Contextual Augmentation Module (CAM) and a Feature Refinement Module (FRM) to enhance the detection performance for small targets. The CAM introduces multi-scale dilated convolutions, where convolutional kernels with different dilation rates capture contextual information from various receptive fields, enabling more accurate extraction of small-target features. The FRM performs adaptive feature fusion in both channel and spatial dimensions, significantly improving the detection precision for small targets. Addressing the issue of a significant disparity in the number of annotations between small and larger objects in existing classic public datasets, a new data augmentation method called Copy–Reduce–Paste is introduced. Ablation and comparative experiments conducted on the proposed STDA-YOLOv8 model demonstrate that on the VisDrone dataset, its accuracy improved by 5.3% compared to YOLOv8, reaching 93.5%; on the PASCAL VOC dataset, its accuracy increased by 5.7% compared to YOLOv8, achieving 94.2%, outperforming current mainstream target detection models and small-target detection algorithms like QueryDet, effectively enhancing small-target detection capabilities. Full article

Apples are a highly valuable economic crop worldwide, but their cultivation often faces challenges from pests and diseases that severely affect yield and quality. To address this issue, this study proposes an improved pest and disease detection algorithm, YOLO-PEL, based on YOLOv11, which integrates multiple innovative modules, including PMFEM, EHFPN, and LKAP, combined with data augmentation strategies, significantly improving detection accuracy and efficiency in complex environments. PMFEM leverages partial multi-scale feature extraction to effectively enhance feature representation, particularly improving the ability to capture pest and disease targets in complex backgrounds. EHFPN employs hierarchical feature fusion and an efficient local attention mechanism to markedly improve the detection accuracy of small targets. LKAP introduces a large kernel attention mechanism, expanding the receptive field and enhancing the localization precision of diseased regions. Experimental results demonstrate that YOLO-PEL achieves a mAP@50 of $72.9\%$ in the Turkey_Plant dataset’s apple subset, representing an improvement of approximately $4.3\%$ over the baseline YOLOv11. Furthermore, the model exhibits favorable lightweight characteristics in terms of computational complexity and parameter count, underscoring its effectiveness and robustness in practical applications. YOLO-PEL not only provides an efficient solution for agricultural pest and disease detection, but also offers technological support for the advancement of smart agriculture. Future research will focus on optimizing the model’s speed and lightweight design to adapt to broader agricultural application scenarios, driving further development in agricultural intelligence technologies. Full article

Mixed Sample Data Augmentation (MSDA) enhances deep learning model generalization by blending a source patch into a target image. Selecting source patches based on image saliency helps to prevent label errors and irrelevant content; however, it relies on computationally expensive saliency detection algorithms. Studies suggest that a convolutional neural network’s receptive field follows a Gaussian distribution, with central pixels being more influential. Leveraging this, we propose GaussianMix, an effective and efficient augmentation strategy that selects source patches using a center-biased Gaussian distribution in order to avoiding additional computational costs. GaussianMix achieves top-1 error rates of 21.26% and 20.09% on ResNet-50 and ResNet-101 for ImageNet classification, respectively, while also improving robustness against adversarial perturbations and enhancing object detection performance. Full article

This research paper explores the impact of immersive virtual reality (IVR) on the teaching of conceptual design in engineering and architecture fields, focusing on the use of interactive 3D drawing tools in virtual and augmented reality environments. The study analyzes how IVR influences spatial understanding, idea communication, and immersive 3D sketching for industrial and architectural design. Additionally, it examines user perceptions of virtual spaces prior to physical construction and evaluates the effectiveness of these technologies through surveys administered to mechanical engineering students utilizing VR/AR headsets. A structured methodology was developed for students enrolled in an industrial design course, comprising four phases: initial theoretical instruction on ephemeral architecture, immersive 3D sketching sessions using Meta Quest 2 and Microsoft HoloLens 2 VR/AR headsets, detailed CAD modeling based on conceptual sketches, and immersive virtual tours to evaluate user perception and design efficacy. Ad hoc questionnaires specifically designed for this research were employed. The results indicate a positive reception to IVR, emphasizing its ease of use, intuitive learning process, and effectiveness in improving motivation, academic performance, and student engagement during the conceptual design phase in graphic engineering education. Full article

The semantic segmentation (SS) of low-contrast images (LCIs) remains a significant challenge in computer vision, particularly for sensor-driven applications like medical imaging, autonomous navigation, and industrial defect detection, where accurate object delineation is critical. This systematic review develops a comprehensive evaluation of state-of-the-art deep learning (DL) techniques to improve segmentation accuracy in LCI scenarios by addressing key challenges such as diffuse boundaries and regions with similar pixel intensities. It tackles primary challenges, such as diffuse boundaries and regions with similar pixel intensities, which limit conventional methods. Key advancements include attention mechanisms, multi-scale feature extraction, and hybrid architectures combining Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs), which expand the Effective Receptive Field (ERF), improve feature representation, and optimize information flow. We compare the performance of 25 models, evaluating accuracy (e.g., mean Intersection over Union (mIoU), Dice Similarity Coefficient (DSC)), computational efficiency, and robustness across benchmark datasets relevant to automation and robotics. This review identifies limitations, including the scarcity of diverse, annotated LCI datasets and the high computational demands of transformer-based models. Future opportunities emphasize lightweight architectures, advanced data augmentation, integration with multimodal sensor data (e.g., LiDAR, thermal imaging), and ethically transparent AI to build trust in automation systems. This work contributes a practical guide for enhancing LCI segmentation, improving mean accuracy metrics like mIoU by up to 15% in sensor-based applications, as evidenced by benchmark comparisons. It serves as a concise, comprehensive guide for researchers and practitioners advancing DL-based LCI segmentation in real-world sensor applications. Full article

Under the condition of low SNR, enhancing the precision of parameter estimation for linear frequency modulation (LFM) signals and diminishing the complexity of the relevant methods represent crucial challenges that are presently being confronted. To address this problem, a parameter estimation method for LFM signals based on the High-Resolution Representation network (HRRnet) is proposed. The fundamental concept underlying this method lies in the employment of a strategy that combines the expansion of the receptive field with the fusion of multi-scale features. This enables the efficient extraction of both global and local information, which in turn augments the expressive power of the inherent signal characteristics and consequently mitigates the impact of noise interference. Based on this strategy, a high-resolution representation of the time–frequency spectrum of the signals is performed to improve the distinguishability of the time–frequency spectrum, and it further improve the accuracy of parameter estimation for LFM signals. In addition, the network utilizes dilated convolution to expand the receptive field while reducing the dependence on network depth, so as to control the network complexity and further optimize the computational efficiency. Experimental results show that when the SNR is greater than −12 dB and the tolerable error is equal to 0.1, the average accuracy of the HRRnet method for estimating the initial frequency and frequency modulation coefficient of LFM signals can reach above 95.53% and 91.19%, respectively, and its number of parameters and computational complexity are reduced to more than 20.47% and 20.37% of those of the existing methods. Full article

Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores—95.3% and 89.5%, respectively—surpassing previous benchmarks. Additionally, we tested the model’s transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management. Full article

Pneumonia is a respiratory infection that affects the lungs. The symptoms of viral and bacterial pneumonia are similar. In order to improve automatic detection efficiency regarding X-ray images of pneumonia, this paper, we propose a novel pneumonia detection method based on the Fast-YOLO network model. First, we re-annotated the open-source dataset of MIMIC Chest X-ray pneumonia, enhancing the model’s adaptability to complex scenes by incorporating Mixup, Mosaic, and Copy–Paste augmentation methods. Additionally, CutMix and Random Erasing were introduced to increase data diversity. Next, we developed a lightweight FASPA Fast Pyramid Attention Mechanism and designed the Fast-YOLO network based on this mechanism to effectively address the complex features in pneumonia X-ray images, such as low contrast and an uneven distribution of local lesions. The Fast-YOLO network improves upon the YOLOv11 architecture by replacing the C3k2 module with the FASPA attention mechanism, significantly reducing the network’s parameter count while maintaining detection performance. Furthermore, the Fast-YOLO network enhances feature extraction capabilities when handling scenes with geometric deformations, multi-scale features, and dynamic changes. It expands the receptive field, thereby balancing computational efficiency and accuracy. Finally, the experimental results demonstrate that the Fast-YOLO network, compared to traditional convolutional neural network methods, can effectively identify pneumonia regions and localize lesions in pneumonia X-ray image detection tasks, achieving significant improvements in FPS, precision, recall, mAP @0.5, and mAP @0.5:0.95. This confirms that Fast-YOLO strikes a balance between computational efficiency and accuracy. The network’s excellent generalization capability across different datasets has been validated, showing the potential to accelerate the pneumonia diagnostic process for clinicians and enhance diagnostic accuracy. Full article