基于改进YOLOv5s算法的危险区域入侵报警

Intrusion Alarm of Dangerous Area Based on Improved YOLOv5s Algorithm

工厂环境复杂多变,存在很多危险区域,违规进入会给工人的生命健康带来严重的危害.针对传统的检测方法操作复杂、识别效果差,提出了一种基于改进YOLOv5s模型的危险区域工人入侵警报系统.首先将基于SGBM算法双目测距技术融合进YOLOv5s目标检测中,增加空间距离这一触发条件,使得工人只有走近摄像头一定范围内才会触发声光报警.进一步地,在YOLOv5s中引入注意力机制,通过对比实验证明了CA模块的引入对模型的平均准确率mAP@0.5提升最明显为1.86%.结果显示此方法能够较为准确的识别出工人是否进入危险区域,并进行声光报警,提醒工人注意,为工厂安全管理提供了新的手段.

The factory environment is complex and changeable, with many dangerous areas, and illegal entry can bring serious harm to the life and health of workers. Considering the complex operation and poor recognition effect of traditional detection methods, this study proposes an alarm system for workers’ intrusion in dangerous areas on the basis of the improved YOLOv5s model. Firstly, the binocular ranging technology based on the SGBM algorithm is integrated into YOLOv5s object detection, and the trigger condition of spatial distance is added. Hence, the sound and light alarm will be triggered only when workers approach the camera within a certain range. Furthermore, the attention mechanism is introduced into YOLOv5s. Comparative experiments prove that the introduction of the CA module improves the average accuracy of mAP@0.5 by 1.86%. The results show that this method can accurately identify the intrusion of a worker in dangerous areas and gives a sound and light alarm to remind the worker, which provides a new means for factory safety management.