基于STA-YOLOv5的水利建造人员安全帽佩戴检测算法

在大型水利建造工程现场，存在高空坠物、塔吊转动、墙体坍塌等问题，对于建造人员人身安全造成巨大威胁，佩戴安全帽是保护建造人员的有效措施，作为工程作业中的安全管理，对建造人员进行安全帽佩戴的精确检测很有必要。针对现有安全帽检测算法在大型水利建造场景下对小且密集的安全帽目标存在漏检、检测精度较低等问题，提出一种基于STA-YOLOv5的安全帽佩戴检测算法，该算法将Swin Transformer和注意力机制引入到YOLOv5算法中，提高模型对安全帽的识别能力。实验结果表明，STA-YOLOv5算法具有更精确检测结果，识别准确率达到91.6%，较原有的YOLOv5算法有明显提升。

Hydraulic construction personnel safety helmet wearing detection algorithm based on STA-YOLOv5

At the site of large-scale water conservancy construction projects, there are problems such as falling objects, tower crane rotation, wall collapse, which pose a great threat to the personal safety of construction personnel. Wearing safety helmets is an effective measure to protect construction personnel. Therefore, it is necessary for construction personnel to carry out accurate detection of helmet wearing as a safety management in engineering operations. Aiming at the problems of missing detection and low detection accuracy of small and dense helmet targets in large-scale hydraulic construction scenarios, a helmet wearing detection algorithm based on STA-YOLOv5 is proposed, which introduces Swin Transformer and attention mechanism into YOLOv5 algorithm to improve the identification ability of the model to the helmet. The experimental results show that the STA-YOLOv5 algorithm has more accurate detection results, and the recognition accuracy reaches 91.6%, which is significantly improved compared with the original YOLOv5 algorithm.