Search Results (168)

Surface defects of photovoltaic (PV) cells can seriously affect power generation efficiency. Accurately detecting such defects and handling them in a timely manner can effectively improve power generation efficiency. Aiming at the high-precision and real-time requirements for surface defect detection during the use of PV cells, this paper proposes a PV cell surface defect detection algorithm based on SEC-YOLOv8. The algorithm first replaces the Spatial Pyramid Pooling Fast module with the SPPELAN pooling module to reduce channel calculations between convolutions. Second, an ECA attention mechanism is added to enable the model to pay more attention to feature extraction in defect areas and avoid target detection interference from complex environments. Finally, the upsampling operator CARAFE is introduced in the Neck part to solve the problem of scale mismatch and enhance detection performance. Experimental results show that the improved model achieves a mean average precision (mAP@0.5) of 69.2% on the PV cell dataset, which is 2.6% higher than the original network, which is designed to achieve a superior balance between the competing demands of accuracy and computational efficiency for PV defect detection. Full article

The conductors of high-speed railway OCSs (Overhead Contact Systems) are susceptible to conductor galloping due to the impact of natural elements such as strong winds, rain, and snow, resulting in conductor fatigue damage and significantly compromising train operational safety. Consequently, monitoring the galloping status of conductors is crucial, and instance segmentation techniques, by delineating the pixel-level contours of each conductor, can significantly aid in the identification and study of galloping phenomena. This work expands upon the YOLO11-seg model and introduces an instance segmentation approach for galloping video and image sensor data of OCS conductors. The algorithm, designed for the stripe-like distribution of OCS conductors in the data, employs four-direction Sobel filters to extract edge features in horizontal, vertical, and diagonal orientations. These features are subsequently integrated with the original convolutional branch to form the FDSE (Four Direction Sobel Enhancement) module. It integrates the ECA (Efficient Channel Attention) mechanism for the adaptive augmentation of conductor characteristics and utilizes the FL (Focal Loss) function to mitigate the class-imbalance issue between positive and negative samples, hence enhancing the model’s sensitivity to conductors. Consequently, segmentation outcomes from neighboring frames are utilized, and mask-difference analysis is performed to autonomously detect conductor galloping locations, emphasizing their contours for the clear depiction of galloping characteristics. Experimental results demonstrate that the enhanced YOLO11-seg model achieves 85.38% precision, 77.30% recall, 84.25% AP@0.5, 81.14% F1-score, and a real-time processing speed of 44.78 FPS. When combined with the galloping visualization module, it can issue real-time alerts of conductor galloping anomalies, providing robust technical support for railway OCS safety monitoring. Full article

In response to the limited research on fire detection in cotton pickers and the issue of low detection accuracy in visual inspection, this paper proposes a computer vision-based detection method. The method is optimized according to the structural characteristics of cotton pickers, and a lightweight improved YOLOv11 algorithm is designed for cotton fire detection in cotton pickers. The backbone of the model is replaced with the MobileNetV2 network to achieve effective model lightweighting. In addition, the convolutional layers in the original C3k2 block are optimized using partial convolutions to reduce computational redundancy and improve inference efficiency. Furthermore, a visual attention mechanism named CBAM-ECA (Convolutional Block Attention Module-Efficient Channel Attention) is designed to suit the complex working conditions of cotton pickers. This mechanism aims to enhance the model’s feature extraction capability under challenging environmental conditions, thereby improving overall detection accuracy. To further improve localization performance and accelerate convergence, the loss function is also modified. These improvements enable the model to achieve higher precision in fire detection while ensuring fast and accurate localization. Experimental results demonstrate that the improved model reduces the number of parameters by 38%, increases the frame processing speed (FPS) by 13.2%, and decreases the computational complexity (GFLOPs) by 42.8%, compared to the original model. The detection accuracy for flaming combustion, smoldering combustion, and overall detection is improved by 1.4%, 3%, and 1.9%, respectively, with an increase of 2.4% in mAP (mean average precision). Compared to other models—YOLOv3-tiny, YOLOv5, YOLOv8, and YOLOv10—the proposed method achieves higher detection accuracy by 5.9%, 7%, 5.9%, and 5.3%, respectively, and shows improvements in mAP by 5.4%, 5%, 4.8%, and 6.3%. The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. These improvements lay a solid foundation for fire prevention and suppression in cotton collection boxes on cotton pickers. Full article

Reconfigurable Intelligent Surface (RIS) has emerged as a promising enabling technology for wireless communications, which significantly enhances system performance through real-time manipulation of electromagnetic wave reflection characteristics. In RIS-assisted communication systems, existing deep learning-based channel state information (CSI) feedback methods often suffer from excessive parameter requirements and high computational complexity. To address this challenge, this paper proposes LwCSI-Net, a lightweight autoencoder network specifically designed for RIS-assisted multiple-input single-output (MISO) systems, aiming to achieve efficient and low-complexity CSI feedback. The core contribution of this work lies in an innovative lightweight feedback architecture that deeply integrates multi-layer convolutional neural networks (CNNs) with attention mechanisms. Specifically, the network employs 1D convolutional operations with unidirectional kernel sliding, which effectively reduces trainable parameters while maintaining robust feature-extraction capabilities. Furthermore, by incorporating an efficient channel attention (ECA) mechanism, the model dynamically allocates weights to different feature channels, thereby enhancing the capture of critical features. This approach not only improves network representational efficiency but also reduces redundant computations, leading to optimized computational complexity. Additionally, the proposed cross-channel residual block (CRBlock) establishes inter-channel information-exchange paths, strengthening feature fusion and ensuring outstanding stability and robustness under high compression ratio (CR) conditions. Our experimental results show that for CRs of 16, 32, and 64, LwCSI-Net significantly improves CSI reconstruction performance while maintaining fewer parameters and lower computational complexity, achieving an average complexity reduction of 35.63% compared to state-of-the-art (SOTA) CSI feedback autoencoder architectures. Full article

Steel surface defect detection is a crucial step in ensuring industrial production quality. However, due to significant variations in scale and irregular geometric morphology of steel surface defects, existing detection algorithms show notable deficiencies in multi-scale feature representation and cross-layer multi-scale feature fusion efficiency. To address these challenges, this paper proposes an improved real-time steel surface defect detection model, NGD-YOLO, based on YOLOv5s, which achieves fast and high-precision defect detection under relatively low hardware conditions. Firstly, a lightweight and efficient Normalization-based Attention Module (NAM) is integrated into the C3 module to construct the C3NAM, enhancing multi-scale feature representation capabilities. Secondly, an efficient Gather–Distribute (GD) mechanism is introduced into the feature fusion component to build the GD-NAM network, thereby effectively reducing information loss during cross-layer multi-scale information fusion and adding a small target detection layer to enhance the detection performance of small defects. Finally, to mitigate the parameter increase caused by the GD-NAM network, a lightweight convolution module, DCConv, that integrates Efficient Channel Attention (ECA), is proposed and combined with the C3 module to construct the lightweight C3DC module. This approach improves detection speed and accuracy while reducing model parameters. Experimental results on the public NEU-DET dataset show that the proposed NGD-YOLO model achieves a detection accuracy of 79.2%, representing a 4.6% mAP improvement over the baseline YOLOv5s network with less than a quarter increase in parameters, and reaches 108.6 FPS, meeting the real-time monitoring requirements in industrial production environments. Full article

With the continuous expansion of the orchard planting area, there is an urgent need for autonomous orchard vehicles that can reduce the labor intensity of fruit farmers and improve the efficiency of operations to assist operators in the process of orchard operations. An object detection system that can accurately identify potholes, trees, and other orchard objects is essential to achieve unmanned operation of the orchard vehicle. Aiming to improve upon existing object detection algorithms, which have the problem of low object recognition accuracy in orchard operation scenes, we propose an orchard vehicle object detection algorithm based on Attention-Guided Orchard PointPillars (AGO-PointPillars). Firstly, we use an RGB-D camera as the sensing hardware to collect the orchard road information and convert the depth image data obtained by the RGB-D camera into 3D point cloud data. Then, Efficient Channel Attention (ECA) and Efficient Up-Convolution Block (EUCB) are introduced based on the PointPillars, which can enhance the ability of feature extraction for orchard objects. Finally, we establish an orchard object detection dataset and validate the proposed algorithm. The results show that, compared to the PointPillars, the AGO-PointPillars proposed in this study has an average detection accuracy improvement of 4.64% for typical orchard objects such as potholes and trees, which can prove the reliability of our algorithm. Full article

To address the issues of high model complexity and weak noise resistance in convolutional neural networks for bearing fault diagnosis, this paper proposes a novel lightweight multi-angle feature fusion convolutional neural network (LMAFCNN). First, the original signal was preprocessed using a wide-kernel convolutional layer to achieve data dimensionality reduction and feature channel expansion. Second, a lightweight multi-angle feature fusion module was designed as the core feature extraction unit. The main branch fused multidimensional features through pointwise convolution and large-kernel channel-wise expansion convolution, whereas the auxiliary branch introduced an efficient channel attention (ECA) mechanism to achieve channel-adaptive weighting. Feature enhancement was achieved through the addition of branches. Finally, global average pooling and fully connected layers were used to complete end-to-end fault diagnosis. The experimental results showed that the proposed method achieved an accuracy of 99.5% on the Paderborn University (PU) artificial damage dataset, with a computational complexity of only 14.8 million floating-point operations (MFLOPs) and 55.2 K parameters. Compared with existing mainstream methods, the proposed method significantly reduces model complexity while maintaining high accuracy, demonstrating excellent diagnostic performance and application potential. Full article

Accurate land parcel segmentation in remote sensing imagery is critical for applications such as land use analysis, agricultural monitoring, and urban planning. However, existing methods often underperform in complex scenes due to small-object segmentation challenges, blurred boundaries, and background interference, often influenced by sensor resolution and atmospheric variation. To address these limitations, we propose a dual-stage framework that combines an enhanced YOLOv5s detector with the Segment Anything Model (SAM) to improve segmentation accuracy and robustness. The improved YOLOv5s module integrates Efficient Channel Attention (ECA) and BiFPN to boost feature extraction and small-object recognition, while Soft-NMS is used to reduce missed detections. The SAM module receives bounding-box prompts from YOLOv5s and incorporates morphological refinement and mask stability scoring for improved boundary continuity and mask quality. A composite Focal-Dice loss is applied to mitigate class imbalance. In addition to the publicly available CCF BDCI dataset, we constructed a new WuJiang dataset to evaluate cross-domain performance. Experimental results demonstrate that our method achieves an IoU of 89.8% and a precision of 90.2%, outperforming baseline models and showing strong generalizability across diverse remote sensing conditions. Full article

Against the backdrop of the deep integration of national fitness and sports science, this study addresses the lack of standardized movement assessment in yoga training by proposing an intelligent analysis system that integrates an improved YOLOv11-ECA detector with the DeepSORT tracking algorithm. A dynamic adaptive anchor mechanism and an Efficient Channel Attention (ECA) module are introduced, while the depthwise separable convolution in the C3k2 module is optimized with a kernel size of 2. Furthermore, a Parallel Spatial Attention (PSA) mechanism is incorporated to enhance multi-target feature discrimination. These enhancements enable the model to achieve a high detection accuracy of 98.6% mAP@0.5 while maintaining low computational complexity (2.35 M parameters, 3.11 GFLOPs). Evaluated on the SND Sun Salutation Yoga Dataset released in 2024, the improved model achieves a real-time processing speed of 85.79 frames per second (FPS) on an RTX 3060 platform, with an 18% reduction in computational cost compared to the baseline. Notably, it achieves a 0.9% improvement in AP@0.5 for small targets (<20 px). By integrating the Mars-smallCNN feature extraction network with a Kalman filtering-based trajectory prediction module, the system attains 58.3% Multiple Object Tracking Accuracy (MOTA) and 62.1% Identity F1 Score (IDF1) in dense multi-object scenarios, representing an improvement of approximately 9.8 percentage points over the conventional YOLO+DeepSORT method. Ablation studies confirm that the ECA module, implemented via lightweight 1D convolution, enhances channel attention modeling efficiency by 23% compared to the original SE module and reduces the false detection rate by 1.2 times under complex backgrounds. This study presents a complete “detection–tracking–assessment” pipeline for intelligent sports training. Future work aims to integrate 3D pose estimation to develop a closed-loop biomechanical analysis system, thereby advancing sports science toward intelligent decision-making paradigms. Full article

Precise quality screening of tea tree seeds is crucial for the development of the tea industry. This study proposes a high-precision quality classification method for tea tree seeds by integrating mid-infrared (MIR) spectroscopy with an improved deep learning model. Four types of tea tree seeds in different states were prepared, and their spectral data were collected and preprocessed using Savitzky–Golay (SG) filtering and wavelet transform. Aiming at the deficiencies of DenseNet121 in one-dimensional spectral processing, such as insufficient generalization ability and weak feature extraction, the ECA-DenseNet model was proposed. Based on DenseNet121, the Batch Channel Normalization (BCN) module was introduced to reduce the dimensionality via 1 × 1 convolution while preserving the feature extraction capabilities, the Attention–Convolution Mix (ACMix) module was integrated to combine convolution and self-attention, and the Efficient Channel Attention (ECA) mechanism was utilized to enhance the feature discriminability. Experiments show that ECA-DenseNet achieves 99% accuracy, recall, and F1-score for classifying the four seed quality types, outperforming the original DenseNet121, machine learning models, and deep learning models. This study provides an efficient solution for tea tree seeds detection and screening, and its modular design can serve as a reference for the spectral classification of other crops. Full article

Underwater object detection with Synthetic Aperture Sonar (SAS) images faces many problems, including low contrast, blurred edges, high-frequency noise, and missed small objects. To improve these problems, this paper proposes a high-accuracy underwater object detection algorithm for SAS images, named the HAUOD algorithm. First, considering SAS image characteristics, a sonar preprocessing module is designed to enhance the signal-to-noise ratio of object features. This module incorporates three-stage processing for image quality optimization, and the three stages include collaborative adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement, non-local mean denoising, and frequency-domain band-pass filtering. Subsequently, a novel C2fD module is introduced to replace the original C2f module to strengthen perception capabilities for low-contrast objects and edge-blurred regions. The proposed C2fD module integrates spatial differential feature extraction, dynamic feature fusion, and Enhanced Efficient Channel Attention (Enhanced ECA). Furthermore, an underwater multi-scale contextual attention mechanism, named UWA, is introduced to enhance the model’s discriminative ability for multi-scale objects and complex backgrounds. The proposed UWA module combines noise suppression, hierarchical dilated convolution groups, and dual-dimensional attention collaboration. Experiments on the Sonar Common object Detection dataset (SCTD) demonstrate that the proposed HAUOD algorithm achieves superior performance in small object detection accuracy and multi-scenario robustness, attaining a detection accuracy of $95.1\%$, which is $8.3\%$ higher than the baseline model (YOLOv8n). Compared with YOLOv8s, the proposed HAUOD algorithm can achieve $6.2\%$ higher accuracy with only $50.4\%$ model size, and reduce the computational complexity by half. Moreover, the HAUOD method exhibits significant advantages in balancing computational efficiency and accuracy compared to mainstream detection models. Full article

Objective: This study proposes a novel deep learning approach for enhancing low-dose bone scintigraphy images using an Enhanced Convolutional Autoencoder (ECAE), aiming to reduce patient radiation exposure while preserving diagnostic quality, as assessed by both expert-based quantitative image metrics and qualitative evaluation. Methods: A supervised learning framework was developed using real-world paired low- and full-dose images from 105 patients. Data were acquired using standard clinical gamma cameras at the Nuclear Medicine Department of the University General Hospital of Alexandroupolis. The ECAE architecture integrates multiscale feature extraction, channel attention mechanisms, and efficient residual blocks to reconstruct high-quality images from low-dose inputs. The model was trained and validated using quantitative metrics—Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)—alongside qualitative assessments by nuclear medicine experts. Results: The model achieved significant improvements in both PSNR and SSIM across all tested dose levels, particularly between 30% and 70% of the full dose. Expert evaluation confirmed enhanced visibility of anatomical structures, noise reduction, and preservation of diagnostic detail in denoised images. In blinded evaluations, denoised images were preferred over the original full-dose scans in 66% of all cases, and in 61% of cases within the 30–70% dose range. Conclusion: The proposed ECAE model effectively reconstructs high-quality bone scintigraphy images from substantially reduced-dose acquisitions. This approach supports dose reduction in nuclear medicine imaging while maintaining—or even enhancing—diagnostic confidence, offering practical benefits in patient safety, workflow efficiency, and environmental impact. Full article

In the field of ceramic product defect detection, traditional manual visual inspection methods suffer from low efficiency and high subjectivity, while existing deep learning algorithms are limited in detection efficiency due to their high complexity. To address these challenges, this study proposes a deep learning-based algorithm for ceramic product defect detection. The algorithm designs a lightweight YOLOv10s detector, which reconstructs the backbone network using GhostNet and incorporates an Efficient Channel Attention (ECA) mechanism fused with depthwise separable convolutions, effectively reducing the model’s complexity and computational load. Additionally, an adaptive threshold method is proposed to improve the traditional Canny edge detection algorithm, significantly enhancing its accuracy in defect edge detection. Experimental results demonstrate that the algorithm achieves an mAP@50 of 92.8% and an F1-score of 90.3% in ceramic product defect detection tasks, accurately identifying and locating four types of defects: cracks, glaze missing, damage, and black spots. In crack detection, the average Edge Localization Error (ELE) is reduced by 25%, the Edge Connectivity Rate (ECR) is increased by 15%, the Weak Edge Responsiveness (WER) is improved by 17%, and the frame rate reaches 40 frames per second (f/s), meeting real-time detection requirements. This algorithm exhibits significant potential in the field of ceramic product defect detection, providing solid technical support for optimizing the ceramic product manufacturing process. Full article

Accurate vehicle type recognition in low-light environments remains a critical challenge for intelligent transportation systems (ITSs). To address the performance degradation caused by insufficient lighting, complex backgrounds, and light interference, this paper proposes a Twin-Stream Feature Fusion Graph Neural Network (TFF-Net) model. The model employs multi-scale convolutional operations combined with an Efficient Channel Attention (ECA) module to extract discriminative local features, while independent convolutional layers capture hierarchical global representations. These features are mapped as nodes to construct fully connected graph structures. Hybrid graph neural networks (GNNs) process the graph structures and model spatial dependencies and semantic associations. TFF-Net enhances the representation of features by fusing local details and global context information from the output of GNNs. To further improve its robustness, we propose an Adaptive Weighted Fusion-Bagging (AWF-Bagging) algorithm, which dynamically assigns weights to base classifiers based on their F1 scores. TFF-Net also includes dynamic feature weighting and label smoothing techniques for solving the category imbalance problem. Finally, the proposed TFF-Net is integrated into YOLOv11n (a lightweight real-time object detector) with an improved adaptive loss function. For experimental validation in low-light scenarios, we constructed the low-light vehicle dataset VDD-Light based on the public dataset UA-DETRAC. Experimental results demonstrate that our model achieves 2.6% and 2.2% improvements in mAP50 and mAP50-95 metrics over the baseline model. Compared to mainstream models and methods, the proposed model shows excellent performance and practical deployment potential. Full article

Urban image semantic segmentation faces challenges including the coexistence of multi-scale objects, blurred semantic relationships between complex structures, and dynamic occlusion interference. Existing methods often struggle to balance global contextual understanding of large scenes and fine-grained details of small objects due to insufficient granularity in multi-scale feature extraction and rigid fusion strategies. To address these issues, this paper proposes an Adaptive Multi-scale Feature Fusion Network (AMFFNet). The network primarily consists of four modules: a Multi-scale Feature Extraction Module (MFEM), an Adaptive Fusion Module (AFM), an Efficient Channel Attention (ECA) module, and an auxiliary supervision head. Firstly, the MFEM utilizes multiple depthwise strip convolutions to capture features at various scales, effectively leveraging contextual information. Then, the AFM employs a dynamic weight assignment strategy to harmonize multi-level features, enhancing the network’s ability to model complex urban scene structures. Additionally, the ECA attention mechanism introduces cross-channel interactions and nonlinear transformations to mitigate the issue of small-object segmentation omissions. Finally, the auxiliary supervision head enables shallow features to directly affect the final segmentation results. Experimental evaluations on the CamVid and Cityscapes datasets demonstrate that the proposed network achieves superior mean Intersection over Union (mIoU) scores of 77.8% and 81.9%, respectively, outperforming existing methods. The results confirm that AMFFNet has a stronger ability to understand complex urban scenes. Full article