Lightweight Six-Class Personal Protective Equipment Detection in Construction Scenes Using YOLO11n

XLiteYOLOv12: a lightweight framework for real-time hazardous object detection

An enhanced YOLOv10 framework for small-object safety helmet detection on construction sites.

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.

Lightweight safety helmet detection algorithm for smart construction sites based on improved yolov8

Safety helmet wearing detection is a crucial component of safety management in construction sites. Traditional detection methods based on manual monitoring are inefficient, while existing deep learning models often suffer from high computational costs and poor detection performance for small or occluded targets. To address these issues, this paper proposes a lightweight detection algorithm named GCW-YOLOv8. Firstly, the Ghost Module is introduced into the backbone network to replace the conventional convolution layers, significantly reducing the number of parameters and floating-point operations (FLOPs) while maintaining feature extraction capability. Secondly, the Coordinate Attention (CA) mechanism is embedded into the neck network to enhance the model's sensitivity to spatial location and channel information, thereby improving the detection accuracy of small targets. Finally, the Wise-IoU (WIoU) loss function is adopted to replace the original CIoU loss, utilizing a dynamic non-monotonic focusing mechanism to optimize the gradient assignment for low-quality samples. Experimental results on the safety helmet dataset show that the proposed algorithm achieves a mean Average Precision (mAP@0.5) of 94.5%, while the inference speed reaches 108 FPS. Compared with the baseline YOLOv8n, our method 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.

Improved lightweight YOLOv11 for foreign object detection in transmission line inspection

In power inspection scenarios, foreign objects are usually difficult to detect because of their small size, complex surrounding backgrounds, and limited computational resources for deployment. To improve detection efficiency under these conditions, this study designs a lightweight YOLOv11-based foreign object detector. StarNet is used to reconstruct the backbone, reducing redundant parameters and computational cost. The C3k2-DSAM module is added to the neck to reinforce fine-grained object features and weaken background interference. In addition, SDIoU is introduced for bounding box regression, which improves the localization of multi-scale targets. Experiments on the self-built dataset show that the improved model obtains 86.5% mAP@0.5, 62.1% mAP@0.5:0.95, and 89.3% Precision at 19.2 GFLOPs, demonstrating competitive accuracy with low computational cost.

SEII-YOLO: a lightweight enhanced object detection model for substation equipment infrared images with applications in IoT embedded devices

AN OPTIMIZED YOLOV10 FRAMEWORK FOR DUAL DETECTION OF SAFETY HELMETS AND LICENSE PLATES

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.