复杂场景下的改进YOLOv7-tiny安全帽佩戴检测算法  

Improved YOLOv7-tiny Helmet Wearing Detection Algorithm in Complex Scenes

作　　者：雷源毅 朱文球 廖欢 LEI Yuanyi;ZHU Wenqiu;LIAOHuan(College of Computer Science,Hunan University of Technology,Zhuzhou Hunan 412007,China)

机构地区：[1]湖南工业大学计算机学院,湖南株洲412007

出　　处：《湖南工业大学学报》2024年第6期93-100,共8页Journal of Hunan University of Technology

基　　金：科技创新2030-“新一代人工智能”基金资助重大项目(2018AAA0100400);湖南省自然科学基金资助项目(2021JJ50058,2022JJ50051)

摘　　要：针对建筑工地复杂场景下安全帽图像因背景信息复杂、光照强度和拍摄角度不同等特点,导致YOLOv7-tiny安全帽检测算法识别精度低,易出现漏检以及误检等问题,提出了一种MCWE-YOLO安全帽检测算法。通过改进Mosaic方法进行数据增强,丰富数据多样性;将CoordAttention注意力模块嵌入YOLOv7-tiny模型中,以增强模型对小目标语义信息的提取能力和全局感知能力;提出一种新的FR-ODConv动态卷积代替Head中的静态卷积,并自适应动态调整卷积核的权值,使模型能够更好地提取不同形状和大小安全帽的关键特征;改进算法的损失函数,以聚焦于普通质量的锚框,提高模型检测整体性能;提出用Res-EDH解耦头结构替换原始的Detect结构,对分类和回归进行解耦,在提升精度的同时降低了延时。最后使用开源GDUT-HWD安全帽数据集进行训练测试,实验结果表明,MCWE-YOLO算法的mAP达到87.3%,相比原始YOLOv7-tiny算法提升了1.6%,对比其他算法的最优值提升了2.1%,能有效提高安全帽检测精度,实现建筑工地安全帽的自动化检测。Aiming at the characteristics of helmet images in complex scenes of construction sites,such as complex background information,different light intensity and shooting angles,which lead to low recognition accuracy of YOLOv7-tiny helmet detection algorithm,and prone to missed detection and false detection,a MCWE-YOLO helmet detection algorithm has been proposed.Data enhancement is performed by improving the Mosaic method to enrich data diversity;the CoordAttention module is embedded in the YOLOv7-tiny model to enhance the model's ability to extract small target semantic information and global perception capabilities;a new FR-ODConv dynamic convolution is proposed to replace the static convolution in the head,and the weights of the convolution kernel are adaptively and dynamically adjusted,so that the model can better extract the key features of helmets of different shapes and sizes;the loss function of the algorithm is improved to focus on anchor boxes of ordinary quality and improve the overall detection performance of the model;a new Res-EDH decoupling head structure is proposed to replace the original detect structure,decouple classification and regression,and reduce latency while improving accuracy.The open source GDUT-HWD helmet dataset was used for training and testing.The experimental results show that the mAP of the MCWE-YOLO algorithm reaches 87.3%,which is 1.6%higher than the original YOLOv7-tiny algorithm and 2.1%higher than the optimal value of other algorithms.It can effectively improve the accuracy of helmet detection and realize the automatic detection of helmets on construction sites.

关 键 词：YOLOv7-tiny CoordAttention注意力机制 FR-ODConv动态卷积 Res-EDH解耦头 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]