Search Results (67)

Cotton represents one of the world’s most significant agricultural commodities. However, severe weed proliferation in cotton fields seriously hampers the development of the cotton industry, making precise weed control essential for ensuring healthy cotton growth. Traditional object detection methods often suffer from computational complexity, rendering them difficult to deploy on resource-constrained edge devices. To address this challenge, this paper proposes AVGS-YOLO, a lightweight and enhanced model employing a Quadruple Synergistic Lightweight Perception Mechanism (QSLPM) for precise weed detection in complex cotton field environments. The QSLPM emphasizes synergistic interactions between modules. It integrates lightweight neck architecture (Slimneck) to optimize feature extraction pathways for cotton weeds; the ADown module (Adaptive Downsampling) replaces Conv modules to address model parameter redundancy; the small object attention modulation module (SEAM) enhances the recognition of small-scale cotton weed features; and angle-sensitive geometric regression (SIoU) improves bounding box localization accuracy. Experimental results demonstrate that the AVGS-YOLO model achieves 95.9% precision, 94.2% recall, 98.2% mAP50, and 93.3% mAP50-95. While maintaining high detection accuracy, the model achieves a lightweight design with reductions of 17.4% in parameters, 27% in GFLOPs, and 14.5% in model size. Demonstrating strong performance in identifying cotton weeds within complex cotton field environments, this model provides technical support for deployment on resource-constrained edge devices, thereby advancing intelligent agricultural development and safeguarding the healthy growth of cotton crops. Full article

Under maize–soybean rotation systems, weeds and crops at the seedling stage in dryland fields exhibit high similarity in morphological structure, scale distribution, and spatial arrangement. In addition, complex illumination conditions, occlusion, and background interference further complicate accurate weed discrimination. To address these challenges, this study proposes an improved YOLOv11n-based weed detection method for seedling-stage crops under dryland rotation conditions, aiming to enhance detection accuracy and robustness in UAV-acquired field images. Three key improvements were introduced to enhance model performance: (1) the incorporation of Dynamic Convolution (DynamicConv) to adaptively strengthen feature representation for weeds with varying morphologies and scales in low-altitude remote sensing imagery; (2) the design of a SlimNeck lightweight feature fusion architecture to improve multi-scale feature propagation efficiency while reducing computational cost; (3) the cascaded group attention mechanism (CGA) is integrated into the C2PSA module, thereby improving discrimination capability under complex background conditions. These results represent consistent improvements over baseline models, including YOLOv5, YOLOv6, YOLOv8, YOLOv11, and YOLOv12. Specifically, detection performance for broadleaf weeds and Poaceae weeds reached mAP@0.5 values of 87.2% and 73.9%, respectively. Overall, the proposed method demonstrates superior detection accuracy and stability for seedling-stage weed identification under rotation conditions, providing reliable technical support for variable-rate herbicide application and precision field management. Full article

To overcome the limitations of existing transmission-line inspection models, including reduced detection precision in complex environments, inadequate performance for small objects and multi-scale targets, and high model complexity, a novel foreign object detection method for transmission lines is proposed in this study, based on an enhanced YOLOv8 architecture. First, the original YOLOv8 backbone is substituted with EfficientNetV2 to achieve model lightweighting while improving detection performance. Second, a Slim-neck module is integrated into the YOLOv8 neck to promote cross-layer information propagation and improve feature perception, which in turn boosts the detection performance on small objects. Meanwhile, an efficient multi-scale attention (EMA) is incorporated to boost multi-scale target detection performance, reduce computational overhead, and strengthen feature representation robustness. Finally, the localization performance of predicted targets is further improved by adopting MPDIoU rather than the original loss function. The experimental results indicate that the proposed method attains 97.7% precision, 95.6% recall, and a 97.5% mAP50, outperforming mainstream detection algorithms in comparative evaluations. Furthermore, relative to the baseline model, the Params and GFLOPs are reduced by 32.1% and 31.6%, respectively, thereby achieving a lightweight design and demonstrating its suitability for transmission-line foreign object detection. Full article

To address the challenges of harvest crate localization caused by varying illumination, partial occlusion, and background interference in unstructured farmland environments, as well as the high costs and low efficiency associated with traditional manual harvesting, this paper proposes FAL-YOLO, a lightweight keypoint detection model. Using YOLOv8n-Pose as the baseline framework, the model integrates a C2f-ContextGuided backbone and a Slim-Neck feature fusion layer. Furthermore, a LSCD-LQE lightweight detection head is designed, and an Inner-MPDIoU loss function is introduced to enhance keypoint detection performance under complex backgrounds and occluded conditions. Experimental results on the self-constructed farmland harvest crate dataset indicate that FAL-YOLO requires only 1.71 M parameters and 4.5 GFLOPs of computational cost, representing reductions of 44.5% and 45.8% compared to YOLOv8n-Pose, while achieving an mAP@0.5 of 94.9%, corresponding to an improvement of 1.2%. Additionally, by establishing correspondences between keypoints and the 3D model through the PnP algorithm, the 3D pose of the crate can be reconstructed, providing reliable spatial input for robotic arm manipulation. The results demonstrate that FAL-YOLO achieves an effective balance between model lightweightness and detection accuracy, providing an efficient solution for automatic identification and grasping of harvest crates in farmland environments. Full article

Accurate monitoring of soybean seedlings in the field is a core component for implementing scientific management during the seedling stage and unlocking yield potential. Traditional manual survey methods are inefficient and highly subjective, making them inadequate for real-time assessment at the field scale. This study addresses challenges such as the small size of individual seedlings, dense inter-plant overlap, blurred boundaries, and complex interferences from soil residue and varying illumination by proposing a high-precision method for soybean seedling instance segmentation and georeferenced localization based on low-altitude (12 m) Unmanned Aerial Vehicle (UAV) imagery. By implementing targeted improvements in the YOLOv11n-seg model, we developed the YOLOv11-seg-SSC model, which integrates the SCSA (Shared Cross-Semantic Space and Progressive Channel Self-Attention) mechanism, the Context-Guided (CG) Block, and a lightweight Slim-Neck structure based on GSConv and VoV-GSCSP. While significantly reducing computational complexity (approximately 9.5 GFLOPs and 2.96 M parameters), the model improved the mean average precision for segmentation (mAP@0.5 Mask) from the baseline of 80.6% to 83.3%, maintained a stable detection mAP@0.5 (Box) at 95.9%, and achieved an overall segmentation precision of 85.1% and recall of 80.3%. This approach not only meets the requirements for near-real-time field processing but also outputs seedling spatial distribution results with true geographic coordinates through georeferenced mapping, thereby providing directly applicable data support for seedling count statistics, missing seedling diagnosis, population spatial pattern analysis, and variable-rate management. This study establishes a complete technical pipeline from precise UAV image segmentation to spatially informed seedling status decision support, offering a theoretical foundation for efficient and accurate monitoring of soybean seedlings in the context of smart agriculture. Full article

Automated inspection of power infrastructure with drones requires processing video streams in real time and performing object recognition from image data with constrained resources. Server-based object recognition algorithms depend on transmitting data over a network and require considerable computational resources. In this study, we present an automated system designed to inspect power infrastructure using drones in real time. The proposed system is implemented on the Rockchip RK3588 platform and uses a lightweight YOLOv8 architecture incorporating a Slim-Neck model with a VanillaBlock module integrated into the backbone. To support real-time operation, we developed a digital video stream processing system (DVSPS) to coordinate multimedia processor (MPP)-based hardware video decoding, with inference performed on a multicore neural processing unit (NPU) using thread pooling. The system can navigate autonomously using a closed-loop machine vision system that computes the latitude and longitude of electrical towers to perform multilevel inspections. The proposed model attained an 84.2% mAP50 and 52.5% mAP50:95 with 3.7 GFLOPs and an average throughput of 111.3 FPS with 34% fewer parameters. These results demonstrate that the proposed method is an efficient and scalable solution for autonomous inspection across diverse operational conditions. Full article

The YOLOv8 model has been shown to offer several advantages in detecting defects on concrete surfaces. However, it is ineffective at achieving multiscale feature extraction and accurate detection of underground structures under complex background conditions. Therefore, this study developed a YOLOv8-PSN model to detect surface defects in underground structures more rapidly and accurately. The model uses PSA (Pyramid Squeeze Attention) and Slim-neck to improve the original YOLOv8. The PSA module is adopted in the backbone and neck network to improve the model’s perception of multiscale features. Meanwhile, a Slim-neck structure is introduced into the Neck part to improve computational efficiency and feature fusion. Then, a dataset comprising six concrete surface defect categories, including cracks and spalling, is built and used to evaluate the performance of the developed YOLOv8-PSN. Experimental results show that, compared with the original YOLOv8, YOLOv10, YOLOv11, SSD, and faster R-CNN, the mAP@50 of YOLOV8-PSN increases by 4.48%, 5.32%, 3.47%,20.03%, and 20.93%, respectively, while still maintaining a high-speed, real-time detection speed of up to 99 FPS. Therefore, the developed model has good robustness and practicality in a complex environment and can effectively and rapidly detect surface defects in underground structures. Full article

Efficient object detection is vital for Remotely Operated Vehicles (ROVs) performing marine debris cleanup, yet existing lightweight designs frequently encounter efficiency bottlenecks when adapted to deeper neural networks. This research identifies a critical “Inverted Bottleneck” anomaly in the Slim-Neck architecture on the YOLO11 backbone, where deep-layer Memory Access Cost (MAC) abnormally spikes. To address this, we propose SvelteNeck-YOLO. By incorporating the proposed EHSCSP module and EHConv operator, the model systematically eliminates computational redundancies. Empirical validation on the TrashCan and URPC2019 datasets demonstrates that the model resolves the memory wall issue, achieving a state-of-the-art trade-off with only 5.8 GFLOPs. Specifically, it delivers a 34% relative reduction in computational load compared to specialized underwater models while maintaining a superior Recall of 0.859. Consequently, SvelteNeck-YOLO establishes a robust, cross-generational solution, optimizing the Pareto frontier between inference speed and detection sensitivity for resource-constrained underwater edge computing. Full article

Medical image segmentation acts as a foundational element of medical image analysis. Yet its accuracy is frequently limited by the scale fluctuations of anatomical targets and the intricate contextual traits inherent in medical images—including vaguely defined structural boundaries and irregular shape distributions. To tackle these constraints, we design a multi-scale feature attention network (MFA-Net), customized specifically for thyroid nodule, skin lesion, and breast lesion segmentation tasks. This network framework integrates three core components: a Bidirectional Feature Pyramid Network (Bi-FPN), a Slim-neck structure, and the Convolutional Block Attention Module (CBAM). CBAM steers the model to prioritize boundary regions while filtering out irrelevant information, which in turn enhances segmentation precision. Bi-FPN facilitates more robust fusion of multi-scale features via iterative integration of top-down and bottom-up feature maps, supported by lateral and vertical connection pathways. The Slim-neck design is constructed to simplify the network’s architecture while effectively merging multi-scale representations of both target and background areas, thus enhancing the model’s overall performance. Validation across four public datasets covering thyroid ultrasound (TNUI-2021, TN-SCUI 2020), dermoscopy (ISIC 2016), and breast ultrasound (BUSI) shows that our method outperforms state-of-the-art segmentation approaches, achieving Dice similarity coefficients of 0.955, 0.971, 0.976, and 0.846, respectively. Additionally, the model maintains a compact parameter count of just 3.05 million and delivers an extremely fast inference latency of 1.9 milliseconds—metrics that significantly outperform those of current leading segmentation techniques. In summary, the proposed framework demonstrates strong performance in thyroid, skin, and breast lesion segmentation, delivering an optimal trade-off between high accuracy and computational efficiency. Full article

Cup rupture failures in strip steel welds can lead to strip breakage, resulting in unplanned downtime of high-speed continuous rolling mills and scrap steel losses. Manual visual inspection suffers from a high false positive rate and cannot meet the production cycle time requirements. This paper proposes a lightweight online cup rupture visual inspection method based on an improved YOLOv10 algorithm. The backbone feature extraction network is replaced with ShuffleNetV2 to reduce the model’s parameter count and computational complexity. An ECA attention mechanism is incorporated into the backbone network to enhance the model’s focus on cup rupture micro-cracks. A Slim-Neck design is adopted, utilizing a dual optimization with GSConv and VoV-GSCSP, significantly improving the balance between real-time performance and accuracy. Based on the results, the optimized model achieves a precision of 98.8% and a recall of 99.2%, with a mean average precision (mAP) of 99.5%—an improvement of 0.2 percentage points over the baseline. The model has a computational load of 4.4 GFLOPs and a compact size of only 3.24 MB, approximately half that of the original model. On embedded devices, it achieves a real-time inference speed of 122 FPS, which is about 2.5, 11, and 1.8 times faster than SSD, Faster R-CNN, and YOLOv10n, respectively. Therefore, the lightweight model based on the improved YOLOv10 not only enhances detection accuracy but also significantly reduces computational cost and model size, enabling efficient real-time cup rupture detection in industrial production environments on embedded platforms. Full article

Campus stairwells, characterized by their crowded nature during certain short periods of time, present a high risk for falls that can lead to dangerous stampedes. Accurate fall detection is crucial for preventing such accidents. However, existing research lacks a detection model that balances high precision with lightweight design and lacks on-site experimental validation to assess practical feasibility. This study addresses these gaps by proposing an enhanced fall recognition model based on YOLOv7, validated through on-site experiments. A dataset on campus stairwell falls was established, capturing diverse stairwell personnel behaviors. Four YOLOv7 improvement schemes were proposed, and numerical comparison experiments identified the best-performing model, combining DO-DConv and Slim-Neck modules. This model achieved an average precision (mAP) of 88.1%, 2.41% higher than the traditional YOLOv7, while reducing GFLOPs from 105.2 to 38.2 and cutting training time by 4 h. A field experiment conducted with 22 groups of participants under small-scale populations and varying lighting conditions preliminarily confirmed that the model’s accuracy is within an acceptable range. The experimental results also analyzed the changes in detection confidence across different population sizes and lighting conditions, offering valuable insights for further model improvement and its practical applications. Full article

Effective monitoring of protected wildlife is crucial for biodiversity conservation. While camera traps provide valuable data for ecological observation, existing deep learning models often suffer from low accuracy in detecting rare species and high computational costs, hindering their deployment on edge devices. To address these challenges, this study proposes YOLO11-APS, an improved lightweight model for protected wildlife detection. It enhances the YOLO11n by integrating the self-Attention and Convolution (ACmix) module, the Partial Convolution (PConv) module, and the SlimNeck paradigm. These improvements strengthen feature extraction under complex conditions while reducing computational costs. Experimental results demonstrate that YOLO11-APS achieves superior detection performance compared to the baseline model, attaining a precision of 92.7%, a recall of 87.0%, an mAP@0.5 of 92.6% and an mAP@0.5:0.95 of 62.2%. In terms of model lightweighting, YOLO11-APS reduces the number of parameters, floating-point operations, and model size by 10.1%, 11.1%, and 9.5%, respectively. YOLO11-APS achieves an optimal balance between accuracy and model complexity, outperforming existing mainstream lightweight detection models. Furthermore, tests on unseen wildlife data confirm its strong transferability and robustness. This work provides an efficient deep learning tool for automated wildlife monitoring in protected areas, facilitating the development of intelligent ecological sensing systems. Full article

The surface quality of industrial gaskets directly impacts sealing performance, operational reliability, and market competitiveness. Inadequate or unreliable defect detection in silicone gaskets can lead to frequent maintenance, undetected faults, and security risks in downstream systems. This paper presents VST-YOLOv8, a trustworthy and secure defect detection framework built upon an enhanced YOLOv8 architecture. To address the limitations of C2F feature extraction in the traditional YOLOv8 backbone, we integrate the lightweight Mobile Vision Transformer v2 (ViT v2) to improve global feature representation while maintaining interpretability. For real-time industrial deployment, we incorporate the Gating-Structured Convolution (GSConv) module, which adaptively adjusts convolution kernels to emphasize features of different shapes, ensuring stable detection under varying production conditions. A Slim-neck structure reduces parameter count and computational complexity without sacrificing accuracy, contributing to robustness against performance degradation. Additionally, the Triplet Attention mechanism combines channel, spatial, and fine-grained attention to enhance feature discrimination, improving reliability in challenging visual environments. Experimental results show that VST-YOLOv8 achieves higher accuracy and recall compared to the baseline YOLOv8, while maintaining low latency suitable for edge deployment. When integrated with secure industrial control systems, the proposed framework supports authenticated, tamper-resistant detection pipelines, ensuring both operational efficiency and data integrity in real-world production. These contributions strengthen trust in AI-driven quality inspection, making the system suitable for safety-critical manufacturing processes. Full article

Underwater target detection is a critical technology for marine resource management and ecological protection, but its performance is often limited by complex underwater environments, including optical attenuation, scattering, and dense distributions of small targets. Existing methods have significant limitations in feature extraction efficiency, robustness in class-imbalanced scenarios, and computational complexity. To address these challenges, this study proposes a lightweight adaptive detection model, YOLOv11-MSE, which optimizes underwater detection performance through three core innovations. First, a multi-scale dilated attention (MSDA) mechanism is embedded into the backbone network to dynamically capture multi-scale contextual features while suppressing background noise. Second, a Slim-Neck architecture based on GSConv and VoV-GSCSPC modules is designed to achieve efficient feature fusion via hybrid convolution strategies, significantly reducing model complexity. Finally, an efficient multi-scale attention (EMA) module is introduced in the detection head to reinforce key feature representations and suppress environmental noise through cross-dimensional interactions. Experiments on the underwater detection dataset (UDD) demonstrate that YOLOv11-MSE outperforms the baseline model YOLOv11, achieving a 9.67% improvement in detection precision and a 3.45% increase in mean average precision (mAP50) while reducing computational complexity by 6.57%. Ablation studies further validate the synergistic optimization effects of each module, particularly in class-imbalanced scenarios where detection precision for rare categories (e.g., scallops) is significantly enhanced, with precision and mAP50 improving by 60.62% and 10.16%, respectively. This model provides an efficient solution for edge computing scenarios, such as underwater robots and ecological monitoring, through its lightweight design and high underwater target detection capability. Full article

Strawberry detection in complex orchard environments remains a challenging task due to frequent leaf occlusion, fruit overlap, and illumination variability. To address these challenges, this study presents an improved lightweight detection framework, DS-YOLO, based on YOLOv8n. First, the backbone network of YOLOv8n is replaced with the lightweight StarNet to reduce the number of parameters while preserving the model’s feature representation capability. Second, the Conv and C2f modules in the Neck section are replaced with SlimNeck’s GSConv (hybrid convolution module) and VoVGSCSP (cross-stage partial network) modules, which effectively enhance detection performance and reduce computational burden. Finally, the original CIoU loss function is substituted with WIoUv3 to improve bounding box regression accuracy and overall detection performance. To validate the effectiveness of the proposed improvements, comparative experiments were conducted with six mainstream object detection models, four backbone networks, and five different loss functions. Experimental results demonstrate that the DS-YOLO achieves a 1.7 percentage point increase in mAP50, a 1.5 percentage point improvement in recall, and precision improvement of 1.3 percentage points. In terms of computational efficiency, the number of parameters is reduced from 3.2M to 1.8M, and computational cost decreases from 8.1G to 4.9G, corresponding to reductions of 43% and 40%, respectively. The improved DS-YOLO model enables real-time and accurate detection of strawberry fruits in complex environments with a more compact network architecture, providing valuable technical support for automated strawberry detection and lightweight deployment. Full article