安全帽在保护施工人员免受事故伤害方面发挥着至关重要的作用。然而,由于种种原因,工人们并没有严格执行戴安全帽的规定。为了检测工人是否佩戴安全帽,本文提出了基于YOLOv8改进的目标检测算法(CPAM-P2-YOLOv8)。在YOLOv8的颈部添加CPA...安全帽在保护施工人员免受事故伤害方面发挥着至关重要的作用。然而,由于种种原因,工人们并没有严格执行戴安全帽的规定。为了检测工人是否佩戴安全帽,本文提出了基于YOLOv8改进的目标检测算法(CPAM-P2-YOLOv8)。在YOLOv8的颈部添加CPAM结构,增强网络对图片的特征提取,在YOLOv8的头部引入处理后的小目标检测层P2。CPAM-P2-YOLOv8提高了目标检测的精确度,实验结果表明,改进模型的精确度达到了91%。与YOLOv8对比,CPAM-P2-YOLOv8的mAP50提高了1.0%,参数量减少了17%,同时通过对比发现,CPAM-P2-YOLOv8比YOLOv8在检测小目标方面更有优势。与YOLOv10对比,CPAM-P2-YOLOv8的mAP50提高1.9%。使用知识蒸馏方法,使CPAM-P2-YOLOv8的精确度进一步提升,达到91.4%。Safety helmets play a crucial role in protecting construction workers from accidents and injuries. However, due to various reasons, workers did not strictly adhere to the rule of wearing safety helmets. To detect whether workers are wearing helmets, this article proposes an improved object detection algorithm based on YOLOv8 (CPAM-P2-YOLOv8). Add CPAM structure to the neck network of YOLOv8 to enhance the feature extraction of safety helmets, and introduce a processed small object detection layer P2 at the head of YOLOv8. CPAM-P2-YOLOv8 improved the accuracy of object detection, and experimental results showed that the improved model achieved an accuracy of 91%. Compared with the YOLOv8 model, CPAM-P2-YOLOv8 improved mAP50 by 1.0% and reduced parameter count by 17%. Through comparison, it was found that CPAM-P2-YOLOv8 has more advantages in detecting small targets than YOLOv8. Compared with YOLOv10, the mAP50 of CPAM-P2-YOLOv8 increased by 1.9%. By using the knowledge distillation, the precision of CPAM-P2-YOLOv8 was further improved to 91.4%.展开更多
文摘安全帽在保护施工人员免受事故伤害方面发挥着至关重要的作用。然而,由于种种原因,工人们并没有严格执行戴安全帽的规定。为了检测工人是否佩戴安全帽,本文提出了基于YOLOv8改进的目标检测算法(CPAM-P2-YOLOv8)。在YOLOv8的颈部添加CPAM结构,增强网络对图片的特征提取,在YOLOv8的头部引入处理后的小目标检测层P2。CPAM-P2-YOLOv8提高了目标检测的精确度,实验结果表明,改进模型的精确度达到了91%。与YOLOv8对比,CPAM-P2-YOLOv8的mAP50提高了1.0%,参数量减少了17%,同时通过对比发现,CPAM-P2-YOLOv8比YOLOv8在检测小目标方面更有优势。与YOLOv10对比,CPAM-P2-YOLOv8的mAP50提高1.9%。使用知识蒸馏方法,使CPAM-P2-YOLOv8的精确度进一步提升,达到91.4%。Safety helmets play a crucial role in protecting construction workers from accidents and injuries. However, due to various reasons, workers did not strictly adhere to the rule of wearing safety helmets. To detect whether workers are wearing helmets, this article proposes an improved object detection algorithm based on YOLOv8 (CPAM-P2-YOLOv8). Add CPAM structure to the neck network of YOLOv8 to enhance the feature extraction of safety helmets, and introduce a processed small object detection layer P2 at the head of YOLOv8. CPAM-P2-YOLOv8 improved the accuracy of object detection, and experimental results showed that the improved model achieved an accuracy of 91%. Compared with the YOLOv8 model, CPAM-P2-YOLOv8 improved mAP50 by 1.0% and reduced parameter count by 17%. Through comparison, it was found that CPAM-P2-YOLOv8 has more advantages in detecting small targets than YOLOv8. Compared with YOLOv10, the mAP50 of CPAM-P2-YOLOv8 increased by 1.9%. By using the knowledge distillation, the precision of CPAM-P2-YOLOv8 was further improved to 91.4%.
