Abstract: In view of the frequent safety accidents of hoisting machinery in industrial plants， this paper proposes a personnel safety alert system in industrial plants based on computer vision， which uses a combination of computing platform and target detection algorithm to detect the personnel targets in the field operation monitoring video in real time and output corresponding control instructions. The target detection algorithm is based on YOLOv5 network， and the attention mechanism is embedded in the network structure. The space and channel based hybrid attention mechanism module is added to BottleneckCSP module， which can improve the accuracy of small target detection. In addition， a person tracking algorithm is introduced to modify and fuse the detection results， which can reduce the missed detection rate when the person is in the occlusion situation. The improved algorithm is tested in the self built dataset. Compared with the original YOLOv5 network， the improved algorithm is 3.414 percentage point higher on the mAP， and the detection speed can reach 40.3 FPS， which has a good detection effect. Finally， the algorithm model is deployed to the computing platform， and is built and tested on the scene. The test statistics showe that the detection accuracy of ordinary personnel and navigators is 94.4% and 95.1%， respectively， which has good detection performance and can stably perform corresponding automatic security alert operations.

Key words: hoisting machinery, personnel safety warning system, object detection, target tracking