Search Results (569)

Hoof slipping in dairy cows is a subtle, transient hoof motion event distinct from lameness or falling, with short duration, limited displacement, and close resemblance to normal gait, making automated detection particularly challenging; relevant methods remain scarce. This study proposes a cascaded detection framework based on improved DeepLabCut and NeuFlow v2 for automated hoof-slipping detection and distance estimation in Holstein dairy cows. The four-stage framework covers hoof key point localization, pixel-level optical flow fusion, motion parameter curve feature extraction, and Random Forest classification. The framework was developed on Dataset 1, which contained 115 single-cow side-view videos. Of these, 31 contained slipping events and 84 were normal walking. It was further assessed on a smaller second-farm dataset of 17 single-cow videos (Dataset 2). ResNet-50 with a Coordinate Attention mechanism was adopted as the backbone, reducing mean four-hoof localization RMSE to 2.80 pixels across five independent training runs, showing a 15.2% improvement over the baseline, and outperforming YOLOv8s-Pose. NeuFlow v2 was applied to extract the localized optical flow from hoof regions, yielding velocity and directional curves from which slipping features were derived. The Random Forest classifier achieved an accuracy of 98.9%, precision of 93.3%, recall of 90.3%, F1 score of 91.8%, and AUC of 0.995, outperforming MViT, SlowFast, and STME. The slipping distance estimation RMSE was 1.22 pixels. With the localisation model retrained on new farm frames, the method reached comparable performance on the second farm, suggesting preliminary cross-farm generalisability that warrants larger-scale validation. The proposed framework provides a non-invasive basis for early hoof-health monitoring and welfare-oriented farm management. Full article

Accurate tomato detection in greenhouse imagery is essential for robotic harvesting, yield estimation, and crop monitoring, yet visual clutter, fruit overlap, partial occlusion, and variable illumination remain challenging for object detectors. Although attention modules are frequently used in agricultural vision studies to improve feature discrimination, their practical contribution is often reported without controlled comparison against strong baseline detectors. This study presents a reproducible and deployment-aware benchmark for single-class greenhouse tomato detection using 895 images with 4930 annotated tomato instances in PASCAL VOC format. The first experimental block used a fixed 70/20/10 split to compare Faster R-CNN, four attention-augmented Faster R-CNN variants, Cascade R-CNN with ResNet101-DCN-FPN, and YOLOv11s attention variants. A second extended protocol converted the annotations to YOLO format and evaluated YOLO-family detectors and RT-DETR-l under a stratified 70/15/15 split, including ablation, robustness, seed-stability, and deployment analyses. The annotation audit confirmed valid bounding boxes, no empty images, and a high proportion of small tomato instances. In the first block, attention integration did not consistently improve detection performance, whereas Cascade R-CNN achieved the highest accuracy with 92.80% ${\mathrm{mAP}}_{0.5}$ and 90.80% $F_1$-score. In the extended protocol, RT-DETR-l obtained the highest test accuracy with 91.49% ${\mathrm{mAP}}_{0.5}$ and 58.59% ${\mathrm{mAP}}_{0.5:0.95}$, while Final-YOLO11s achieved comparable performance with lower latency, reaching 91.42% ${\mathrm{mAP}}_{0.5}$, 58.37% ${\mathrm{mAP}}_{0.5:0.95}$, and 86.19% $F_1$-score. Across three seeds, Final-YOLO11s obtained a stable mean ${\mathrm{mAP}}_{0.5}$ of 90.84%. Robustness analysis showed that motion blur and Gaussian noise caused the largest degradation, whereas compact YOLO models exported reliably to ONNX and TensorRT. Overall, the results indicate that localization quality, robustness, latency, model size, stability, and export capability should be considered together, and that adding attention modules by default is less reliable than evidence-driven detector selection. Full article

In Unmanned aerial vehicle (UAV) forest fire detection, flames and smoke exhibit dramatic scale variations. Existing methods often struggle with multi-scale feature extraction, fusion quality, and localization reliability, resulting in limited accuracy improvements. To address this issue, this study optimizes the backbone, neck, and head of YOLOv11n to propose a novel multi-scale forest fire detector (MFFDet), which consists of three key modules: (1) the Multi-Scale Feature Calibration Module (MFCM) is designed to improve multi-scale feature representation by context aggregation and detail calibration; (2) the Cross-Scale Semantic Alignment Module (CSAM) is proposed to enhance fusion quality by applying channel reorganization and local spatial refinement; and (3) the Location Quality Estimator Head (LQEH) is presented for reliable localization by mapping the statistical information of regression distributions into a localization quality score, which systematically boosts the accuracy and stability of multi-scale object detection. In addition, to alleviate the scarcity of UAV forest fire detection data, this study constructs a UAV Forest Fire Dataset (UF${}^2$D), providing important data support for UAV-based fire detection. Experiments on UF${}^2$D show that MFFDet achieves an mAP@0.5 of 70.1%, the best among all compared models, representing a 4.4% improvement over the baseline. Moreover, it attains the top performance on small, medium, and large objects, with AP${}_s$ of 20.3%, AP${}_m$ of 31.5%, and AP${}_l$ of 44.8%, highlighting MFFDet’s robustness and accuracy for multi-scale flame and smoke detection in a complex forest fire environment, which bears important practical significance for the intelligent upgrade of forest fire prevention and control. Full article

As unmanned aerial vehicles (UAVs) become central to traffic inspection, urban security, and emergency response, UAV-based environmental perception requires both high accuracy and real-time efficiency. However, UAV imagery remains challenging due to three primary factors: detail loss, where small targets occupy minimal pixels and weak edges are diluted by downsampling; ineffective cross-scale fusion, where semantic gaps between shallow and deep features lead to scale misalignment and small-object suppression; and environmental interference, where clutter, occlusion, and dense layouts cause localization drift. To address these challenges, we propose an optimized efficient detector built upon the YOLOv8s framework, incorporating multi-scale feature enhancement and saliency-guided cross-layer fusion. Specifically, we integrate RFCAConv and RGCSP modules into the backbone to strengthen local detail and spatial structure modeling. Furthermore, we design a Multi-Scale Adaptive Fusion Module (MSAFM) to align deep and shallow cues through dual-pooling and adaptive channel recalibration. To handle complex backgrounds, a Saliency-Guided Contextual Attention Module (CASM) is introduced to emphasize target regions, alongside a dynamic detection head for adaptive feature modulation. Evaluated on the VisDrone2019 dataset, our method achieves 48.3% mAP@0.5 and 29.0% mAP@[0.5:0.95], outperforming YOLOv8s by 10.2 and 6.3 points, respectively, while keeping the model compact with 7.2M parameters and a 14.4 MB model size. Full article

RGB-T object detection combines visible texture information with thermal saliency cues to improve detection under degraded illumination. Existing RGB-T fusion methods usually perform feature interaction in the spatial domain or treat spectral responses jointly, which may allow coarse background components to dominate the fusion process while weakening boundary and small-target details. In addition, the repeated upsampling and aggregation operations in the detection neck can further smooth high-frequency responses preserved during early fusion. This paper proposes F²Net, a frequency-guided RGB-T object detection framework built on a dual-stream YOLOv11s architecture. The method decomposes RGB and thermal features into low- and high-frequency components for separate cross-modal fusion, mitigates detail attenuation during neck decoding, and regularizes spatial correspondence between RGB and thermal representations during training. On M3FD, F²Net achieves 89.6% mAP@0.5 and 62.1% mAP@0.5:0.95, improving the Dual-YOLOv11s baseline by 7.7 and 6.6 percentage points, respectively, while increasing the parameter count from 13.8M to 15.4M and GFLOPs from 33.9G to 35.6G. Additional experiments on LLVIP and KAIST evaluate the method under low-light and road-scene conditions. The KAIST results show that high-IoU localization remains challenging in dense and occluded pedestrian scenes. This indicates that frequency-guided fusion mainly strengthens target response generation and moderate-IoU detection, but it does not fully solve precise boundary regression under severe occlusion and weak contour conditions. Full article

X-ray inspection is crucial for pipeline weld non-destructive testing (NDT), yet automatic defect detection remains challenging due to low contrast, complex backgrounds, and significant variations in defect morphology. To address these issues, this paper proposes an improved YOLOv11-based method for X-ray weld images, integrating Smooth IoU (SIoU) bounding box regression and P2 shallow feature enhancement. First, to enhance the localization accuracy of elongated Region of Interest (ROI) targets in small-diameter pipe welds, the original CIoU loss is replaced with SIoU loss. By introducing an Angle Cost term, SIoU provides explicit directional constraints, guiding the predicted bounding box to align with the ground-truth orientation. Experimental results show the YOLOv11s + SIoU model achieves 99.5% mAP@50 and 99.9% precision, outperforming the baseline. Second, to improve the detection of larger defects (e.g., lack of fusion, incomplete penetration, and cracks) in long-distance pipeline welds, a P2 detection layer (stride 4) is added. This layer preserves high-resolution spatial details and shallow edge features that are typically lost during deep downsampling. Evaluated on a 960 × 960 input resolution, the YOLOv11s + P2 model achieves 93.07% precision, 94.8% mAP@50, and 72.01% mAP@50–95. The proposed method effectively combines directional constraint with shallow feature preservation, providing a robust solution for both ROI localization and large defect recognition in complex weld X-ray images. Full article

Reliable visual detection of small floating objects on the water surface is a prerequisite for environmental monitoring and clean-up tasks performed by unmanned surface vehicles (USVs) on inland waterways. Such scenes are routinely degraded by low illumination at dawn and dusk, strong specular reflections, ripple-induced clutter, and large object-scale variations, which together cause missed detections, false alarms, and unstable localization. Aiming at these practical challenges, this study conducts a scenario-oriented optimization and experimental validation based on the lightweight YOLOv11n detector. We integrate multiple mature attention mechanisms, regression loss functions and data augmentation strategies to develop an improved scheme, YOLOv11-LREP, for floating object detection. The detailed optimizations are as follows: (i) a Coordinate Attention (CoordAtt) module is inserted at the top of the backbone to enhance positional encoding and highlight obstacle-related semantic regions; (ii) three Efficient Channel Attention (ECA) modules are embedded at the multi-scale fusion nodes of the Neck so that reflection- and ripple-induced spurious channel responses can be suppressed at almost no extra cost; (iii) the Powerful-IoU (PIoU) loss replaces the original regression loss to enforce four-side boundary alignment and stabilize convergence on small, blurred-edge targets; and (iv) a joint low-light and reflection augmentation strategy, together with CutMix region-level mixing, broadens the training distribution along the illumination and occlusion axes. Experiments on the public FloW-Img dataset, split into 1200 training and 800 validation images (2024 instances) and run under a fixed random seed (seed = 0, deterministic = true), show that YOLOv11-LREP attains AP₅₀ = 80.1%, AP_50:95 = 38.5%, and AP_S = 24.3% with only 2.84 M parameters and 9.3 GFLOPs. On an NVIDIA RTX 4060 Laptop GPU, the model runs at 3.3 ms total per 640 × 640 image (≈303 FPS), satisfying real-time perception requirements while retaining lightweight deployability. The ablation results indicate that different components contribute differently to localization accuracy, small-object sensitivity, and robustness, and that the final configuration provides a balanced trade-off rather than the best value for every individual metric. A systematic threshold sensitivity analysis (F₁ fluctuation < 0.2%) demonstrates the stability of the final model. Full article

Reliable selective picking of long-staple cotton remains challenging because dense dwarf canopies restrict robot operating space and increase boll occlusion, resulting in reduced target visibility and potential fiber damage during picking. To address these challenges, a mobile dual-arm robotic picking system integrating hierarchical depth perception, cotton-boll recognition, optimized motion planning, and three-finger flexible end-effectors was developed for autonomous picking in Xinjiang long-staple cotton fields. The proposed YOLOv7-DCN-SENet model reached 95.75% precision, 92.65% recall, and 97.19% mAP@0.5 on the test set, while the onboard computing platform operated at 101 FPS under the experimental configuration. Indoor and field experiments were conducted on directly visible upper-canopy open cotton bolls. The dual-arm robot achieved parallel picking success rates of 74.6% and 57.6%, with average picking cycles of 28.2 s and 34.9 s, respectively. Field performance was mainly limited by strong-light overexposure, depth-information loss, occlusion-induced localization errors, arm interference within narrow canopy spaces, and incomplete fiber separation during boll detachment. These results demonstrate the feasibility of autonomous dual-arm selective picking for long-staple cotton under dense planting conditions and provide a basis for further improvements in robotic cotton-picking systems. Full article

In response to the challenges in UAV-oriented ground small-object localization and detection, including the easy loss of tiny target features, insufficient scale adaptability, severe interference from complex backgrounds, as well as high missed and false detection rates and the inadequate localization accuracy of the conventional YOLOv11n model in such scenarios, this paper takes YOLOv11n as the basic framework and performs systematic optimization from three aspects, network structure, core modules, and feature enhancement, proposing a lightweight small-object-enhanced detection algorithm named YOLOSO for UAV applications. By introducing a P2 high-resolution feature branch with a stride of 4, a four-scale detection structure consisting of P2-P3-P4-P5 is constructed, which reduces the minimum detection stride from 8 to 4 and alleviates the loss of detailed feature information for ultra-tiny targets. A bidirectional “top-down + bottom-up” multi-scale feature fusion strategy is utilized to improve the complementation between deep semantic information and shallow detailed features, while the core modules C3k2SO and C2PSASO are optimized and redesigned, respectively; by adjusting the channel compression ratio (0.25 for shallow modules and 0.75 for deep modules in C3k2SO; 0.25 in C2PSASO), optimizing the convolution kernel configuration (combining 1 × 3 and 3 × 1 convolutions), increasing the number of attention heads (from 4 to 8), and introducing residual connections with a 1 × 1 convolutional branch, the refinement and focusing ability of small-object feature extraction are improved. Additionally, an Enhanced Dual-branch Convolutional Block Attention Module (ED-CBAM) is proposed to further suppress background interference. Experimental results on the VisDrone2019-DET dataset demonstrate that the proposed YOLOSO contains 3.56M parameters and maintains a lightweight structure, attaining P, R, and mAP50 values of 47.2%, 36.8%, and 37.3% in the test set, which are 4.5 percentage points, 4.8 percentage points, and 3.7 percentage points higher than those of the baseline YOLOv11n (42.7%, 32.0% and 33.6%), respectively. Meanwhile, the medium-to-large version YOLOSO-S (14.85M parameters, 45.3% mAP50) reduces the number of parameters by 53.6% compared with the same-scale Rtdetr-L (32.0M) while achieving significantly better performance (37.8% mAP50). Experiments on the DOTAv1 dataset further confirm the generalization of YOLOSO, achieving 62.2% precision and 27.3% mAP50, outperforming all compared YOLO models. Evaluated on the DOTA-v1 dataset, YOLOSO achieves a feasible FPS of 20.53. Although slightly slower than mainstream lightweight YOLO models, the substantial accuracy gains fully offset the minor inference speed loss, and such performance trade-off is acceptable for practical UAV deployment. Ablation experiments verify that structural optimization (2.8 percentage points mAP50 improvement, from 33.6% to 36.4%) and the proposed C2PSASO (0.7 percentage points mAP50 improvement to 34.3%) and C3k2SO (1.4 percentage points mAP50 improvement to 35.0%) modules all contribute positive performance gains with favorable complementarity. While retaining lightweight characteristics, the model effectively enhances the detection accuracy of small objects in unmanned aerial vehicle scenarios and can provide technical references for practical applications such as remote sensing monitoring and security patrolling. Full article

Tomato harvesting still relies heavily on manual labor, while factors such as clustered fruit growth, inconsistent ripening stages, occlusion, and complex cultivation environments pose significant challenges to automated harvesting systems and place higher demands on target detection accuracy. To address these issues, a tomato detection method based on the Mamba state space model was proposed, and an improved model termed YOLO-VCW was developed based on YOLOv8n. Specifically, the original C2f module in the backbone network was replaced with the C2f-VSS module to enhance global contextual feature extraction. A Coordinate Attention mechanism was introduced into the feature fusion stage to improve the model’s ability to focus on tomato target regions under complex background and occlusion conditions. In addition, the WIoUv3 loss function was adopted in the detection head to improve localization accuracy and training stability in overlapping fruit scenarios. Experimental results showed that YOLO-VCW achieved a precision of 91.33%, a recall of 86.79%, and an F1-score of 89.00% on the tomato dataset. Compared with YOLOv8n, the proposed model improved precision, recall, F1-score, and mAP₅₀ by 1.90%, 4.43%, 3.25%, and 4.44%, respectively, with only a slight increase in Parameters to 3.9 M. These results demonstrate that YOLO-VCW provides effective and robust performance for tomato target detection in complex environments. Full article

In this work, a lightweight framework enabled by the modified YOLOv11s-seg model for tea shoot detection and plucking point localization is proposed. Detecting tea shoots and localizing plucking points with higher accuracy generally require larger model size and more model parameters, making it difficult to balance accuracy and lightweighting. To overcome this limitation, a modified lightweight YOLOv11s-seg model is developed. First, the multi-scale edge information enhancement is introduced into the conventional YOLOv11s-seg to extract edge feature better and improve the detection accuracy of tea shoots. Meanwhile, context anchor attention is utilized to modify the cross stage partial spatial attention module in a backbone network to improve the detection capability for small objects. Moreover, the detail calibration reconstruction feature pyramid network is proposed. It utilizes spatial and contextual semantic information to reconstruct and calibrate features in key regions, enhancing the capability for object fusion and recognition at various scales. Furthermore, with the modified model performing instance segmentation to acquire the contour of each tea shoot, the coordinates of the three lowest pixel points in the contour are captured to localize the plucking point based on the average coordinates. In addition, the layer-adaptive magnitude-based pruning (LAMP) method is used to lighten the model. The experimental results show that the LAMP-pruned modified YOLOv11s-seg model with a speedup ratio of 1.5 achieves a mAP@0.5 of 86.5% for tea shoot detection, exhibiting a 4.7 percentage point improvement over the conventional YOLOv11s-seg model. Moreover, it exhibits an accuracy of 81.9% for plucking point localization on the validation and test subsets with 232 images in total, and its number of parameters, model size and floating point operations (FLOPs) separately achieve reductions of 67.3%, 66.2%, and 24.9% over the conventional model as well. Therefore, the proposed LAMP-pruned modified model shows good balance between lightweighting and detection accuracy. Finally, the modified LAMP-pruned YOLOv11s-seg model is deployed on a Jetson Orin NX edge module and measured in a tea plantation, with the measured results exhibiting a detection speed of 34.1 FPS and verifying its availability in practical applications. Full article

To address the challenges of detecting sugar orange fruits in complex natural orchard environments—where fruits are frequently occluded by leaves and branches and may be mutually occluded due to dense growth, leading to missed detections, false positives, and low detection confidence—we propose an improved algorithm based on YOLOv8n, named YOLO-FSEP. A Spatial-Channel Synergistic Attention (SCSA) module is introduced into the main network to enhance feature extraction capabilities; the IoU loss function is replaced with Focal_SIOU to improve the detection accuracy for difficult samples; and an SE attention mechanism is embedded in the detection head, with the addition of a P6 high-resolution detection layer to optimize multi-scale object performance. Experimental results on a self-built sugar orange dataset show that, compared to the baseline YOLOv8n, the improved model achieves a 0.9% increase in accuracy, a 1.3% increase in recall, and a 3.2% increase in mAP50-95, while maintaining an inference speed of 62.6 FPS. To evaluate the model under dynamic conditions, we performed a 200-frame continuous test of the 3D localization pipeline on a laptop with a RealSense D435i camera. The average YOLO inference time was 49.90 ms, post-processing (depth extraction and 3D coordinate conversion) took 0.24 ms, and the total processing time was 50.15 ms. Given that the typical response time for a robotic arm’s single positioning operation is 100–200 ms, this real-time performance meets the dynamic localization requirements of sugar orange harvesting. Full article

Steel strip surface defect detection remains challenging because defects are often elongated, weakly bounded, low-contrast, and sensitive to imaging degradation. To address these issues, this paper proposes Orthogonal Direction-Adaptive YOLOv8n (OAD-YOLOv8n), a lightweight detector based on You Only Look Once version 8 nano (YOLOv8n) and centered on Orthogonal Direction-Adaptive Efficient Multi-Scale Attention (OA-EMA), an orthogonal direction-adaptive attention module that combines debiased strip descriptors, adaptive direction selection, and local directional convolution. Dynamic upsampling by learning to sample (DySample), a lightweight neck structure (SlimNeck), and Adaptive Threshold Focal Loss (ATFL) are further integrated to improve detail-preserving upsampling, efficient multi-scale fusion, and hard-sample optimization. Across five independent runs on NEU-DET, OAD-YOLOv8n improves Precision, Recall, mAP50, and mAP50:95 by 5.0, 3.6, 4.4, and 3.7 percentage points over YOLOv8n, while reducing FLOPs and parameters by approximately 10.3% and 7.0%, respectively. Complementary experiments on GC10-DET, cross-dataset transfer/adaptation, simulated practical image perturbations, failure cases, and measured inference speed provide a broader characterization of the model’s benchmark-level generalization, robustness, and deployment-related behavior. These results indicate that OAD-YOLOv8n provides an effective accuracy–efficiency trade-off for lightweight steel strip surface defect detection. Full article

Object detection in UAV remote sensing imagery remains challenging due to severe scale variation, dense object distributions, complex background clutter, and localization ambiguity caused by extremely small objects. To address these issues, this paper proposes BDRNet, a lightweight background-aware dynamic-scale routing network for UAV remote sensing object detection. First, a background-aware feature enhancement (BAFE) module is introduced into the backbone to enhance feature representation through horizontal and vertical contextual modeling, improving target-related responses in complex aerial scenes. Second, a dynamic-scale routing pyramid (DSRP) is designed to retain the high-resolution $P_2$ branch and adaptively integrate multi-scale features through spatially dynamic routing, alleviating the loss of fine-grained information and improving the representation of small and scale-varied objects. Third, a scale- and geometry-aware normalized Wasserstein distance (SGNW) loss is proposed by modeling bounding boxes as two-dimensional Gaussian distributions. By incorporating aspect-ratio-guided geometric weighting and scale-aware dynamic fusion, SGNW improves regression stability for small objects while preserving geometric constraints for medium and large targets. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate that BDRNet consistently improves detection accuracy over the YOLOv10s detector while maintaining a comparable model size and computational cost. Compared with several mainstream lightweight detectors, BDRNet achieves a favorable accuracy–efficiency trade-off, demonstrating its effectiveness for UAV remote sensing object detection in complex aerial scenarios. Full article

The steel surface defect detection is crucial for steel quality and usage safety. The high computational cost and low detection accuracy are still the main issues in current steel detection models. To efficiently address the issues above, this paper proposes a new steel surface defect detection model named DIDW-YOLOv11. In the proposed DIDW-YOLOv11, the YOLOv11 C3k2 module is first innovatively improved by C3K2-DIMB, which integrates C3K2 and DIMB by introducing DynamicInceptionDWConv2d (DIDW) to sufficiently strengthen the detailed feature extraction for tiny defects and weak-texture defects, improving the matching degree of multi-scale receptive fields. Then the YOLOv11 SPPF module is enhanced by integrating the IDWFSPPF module for optimizing the fusion of local and global information, which combines average pooling and max pooling to enhance the model’s multi-scale feature fusion capability. An auxiliary detection head (ADH) is finally proposed with an additional coarse loss function to process shallow feature information into the model, which uses extra supervision for shallow features to suppress background noise and reduce false detections. Experimental results on the NEU-DET and GC10-DET datasets show that DIDW-YOLOv11 achieves 4.9% and 3.8% improvements in mAP@0.5 compared to the baseline model YOLOv11s. Our research indicates that DIDW-YOLOv11 exhibits stronger recognition ability and robustness in complex and diverse defect detection, providing an effective solution for steel defect detection in industrial production. In addition, experimental results show that our model offers improved performance over the baseline methods. Full article