Small-target worker detection on construction sites
The accurate detection of workers using construction site surveillance videos or images can support the intelligent management of construction safety.However,the long-distance surveillance causes workers to appear as small targets in images,and the complex and changing environment further complicates worker detection.To address this problem,a small-target worker detection method integrating an improved YOLO model with the frame difference method was proposed.On the one hand,the stationary workers were detected through the improved YOLOv5 model,which introduced slicing aided inference to obtain the clearer features of small-target workers,added small-target detection heads to ensure feature completeness,and employed the ECA mechanism to improve the detection performance.On the other hand,the moving workers with weak image features were detected using the frame difference method,to some extent compensating for the shortcomings of image detection.The proposed method was validated on a self-constructed dataset,and the results showed that the F1-Score and mAP50 of the improved YOLOv5 model improved by 11.3%and 12.5%,respectively.Additionally,the detection rate of small-target workers using the integrated method increased by 3.6%to 84.2%,with a FPS of 6 frames·per second.Thus,the proposed method can better satisfy the needs of worker detection on construction sites.
site worker detectionsmall target detectionYOLOframe difference method
NETL
NSTL
万方数据
