Abstract
Automatic Welding Helmet Use (WHU) detection technology is of great significance for the safety management of construction site, then, this paper proposes a lightweight face-assisted model using YOLOv5s for the detection of WHU (WHU-YOLO). First, the Ghost module is introduced into YOLOv5s to optimize feature extraction parts of backbone and neck, reducing model complexity. Then, the neck of YOLOv5s is reconstructed based on Bi-directional Feature Pyramid Network (Bi-FPN). The experimental results implemented on established Welding helmet and Human face Detection (WHD) dataset indicate that the false positives have been greatly decreased with the assistance of face data and the mean average precision (mAP) reaches 83.65%. Meanwhile, under the environment of NVIDIA GeForce GTX 1070 and 640 × 640 input size, WHU-YOLO with inference time up to 5.7 ms achieves model compression with 35.7%, 34.4% and 30.1% reductions in parameters, weight size and Floating Point Operations (FLOPs) compared with YOLOv5s, respectively, which has no decline on detection performance.
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