Findings indicate that the YOLOv10 detector is a strong candidate for deployment-focused helmet-compliance monitoring in large-scale construction settings, although performance limitations remain most evident in far-field views and highly crowded scenes.
Abstract
Safety helmet compliance monitoring remains challenging because helmets often occupy only a few pixels in wide-area surveillance images. This study tackles these small-object difficulties in vision-based safety-helmet compliance monitoring (helmet vs. no-helmet) using wide-area surveillance imagery. An enhanced You Only Look Once version 10 (YOLOv10) detector is proposed by integrating Omni-Dimensional Dynamic Convolution (ODConv) into the backbone, an Efficient Multi-scale Attention-guided Bidirectional Feature Pyramid Network (EMA-BiFPN) for multi-scale feature fusion, a four-head detection scheme, and the Minimum Points Distance Intersection over Union (MPDIoU) regression loss. Across the experiments, the proposed detector reached an mAP50 of 94.28, with AP50 values of 96.55 for the helmet class and 92.01 for the no-helmet class, exceeding the performance of the YOLOv10 baseline and other benchmark detectors. The most notable gains appeared for extremely small and small targets ([Formula: see text] = 86.10, [Formula: see text] = 91.55), reflecting improved localization of helmets at long distances. Overall, these findings indicate that the method is a strong candidate for deployment-focused helmet-compliance monitoring in large-scale construction settings, although performance limitations remain most evident in far-field views and highly crowded scenes.
This study suggests an enhanced safety helmet detection method based on YOLOv10 to solve the low detection accuracy of current algorithms for small objects and complicated settings in different situations.
Iqra Aziza Khatoon, Dr. Safia Khanam· International Journal of Dat...· 0 citations
A lightweight detection algorithm named GCW-YOLOv8, which improves detection accuracy by 1.4% while reducing parameter count by 34.4%, achieving a superior balance between accuracy and efficiency for smart construction site applications.
Zheng Re, Zhisen Ren, Qianru Liu et al.· International Conference on...· 0 citations
The YOLOv12 network is adopted as the baseline model and the ADown module is introduced to improve downsampling efficiency while maintaining lightweight performance, and the BN-CGLU is incorporated into the A2C2f module to enhance the model’s nonlinear representation capability.
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An Adaptive and Scalable YOLO model named AS-YOLOR (Adaptive and Scalable YOLO for Rotated object detection), based on the YOLOv8 baseline is proposed, providing a solution with strong practical potential for achieving efficient and high-precision detection of small, rotated objects.
Jin Huang, Juntao Shen, Min Wang et al.· Applied Sciences· 0 citations
Small-object detection in unmanned aerial vehicle (UAV) aerial images remains challenging because targets usually occupy only a few pixels and are easily affected by complex backgrounds, occlusion, and illumination changes. To address these problems, this study proposes BIDC-YOLO, an improved YOLOv8s-based detector for UAV small-object detection. The model is redesigned from four aspects. First, a C2f_iSE feature enhancement module is constructed by integrating inverse residual mobile block, squeeze-and-excitation, and efficient multi-scale attention mechanisms into the C2f structure to strengthen spatial and channel feature representation. Second, the original large-object detection branch is replaced with a 160 × 160 small-object detection branch, and bidirectional feature pyramid network is introduced to improve bidirectional cross-scale feature fusion. Third, DySample is used to reduce spatial misalignment during upsampling and preserve fine details of small objects. Finally, cross-layer local attention head is incorporated to enhance local semantic-detail alignment in the detection stage. Experiments on the VisDrone2019 dataset show that BIDC-YOLO improves Precision, Recall, mAP50, and mAP50-95 by 9.6, 10.4, 13.1, and 8.9 percentage points, respectively, compared with YOLOv8s. The results indicate that BIDC-YOLO improves the detection of dense and occluded small objects in UAV aerial scenes.
Unknown authors· Engineering Research Express· 0 citations
The installation of a real-time visual tracking system with an active pan-tilt camera for indoor human motion detection is presented, which shows that the inclusion of YOLOv10 significantly improves detection precision and temporal consistency.
Ayman Javid Hussain, Lalitha Saroja Ch, Ruqiya Fatima· International Journal of AI...· 0 citations
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