在施工现场中, 安全帽能够减轻对头部的伤害, 且不同颜色的安全帽代表不同的身份, 基于当前施工现场通过视频监控来对工人安全帽的佩戴以及工种身份识别存在一定的耗时性, 不完全性, 监督效率低等问题, 本文提出了一种基于YOLOv4改进的安全帽佩戴检测以及身份识别的方法, 在原始的YOLOv4的基础之上, 使用K-means算法对先验框的大小重新进行聚类分析处理, 增加多尺度预测输出, 实验DIoU NMS进行非极大值抑制, 从而使工人安全帽佩戴及身份识别达到高效性, 全面性. 结果表明, 佩戴红、蓝、黄、白安全帽和未佩戴安全帽工人平均检测准确率达到92.1%, 从而保证能够实现对施工现场工人安全帽的佩戴达到一种实时监控.
On a construction site, safety helmets can reduce head injuries, and safety helmets of different colors represent different identities. The contemporary method of detecting safety helmet wearing and identifying types of work by video surveillance is time-consuming, incomplete, and low in supervision efficiency. In response, this study proposes an improved method of safety helmet wearing detection and identity recognition based on the you only look once version 4 (YOLOv4). On the basis of the original YOLOv4, the K-means algorithm is used to cluster the size of the prior box again, and multi-scale prediction output is added. The experimental distance intersection over union–non-maximum suppression (DIoU–NMS) is used for NMS so that safety helmet wearing detection and identity recognition of workers can achieve high efficiency and comprehensiveness. The results show that the average detection accuracy among workers wearing red, blue, yellow, and white safety helmets and workers without safety helmets is 92.1%, which means the proposed method ensures the real-time monitoring of the safety helmet wearing of workers on the construction site.