现代电子技术2024，Vol.47Issue(24)：160-166,7.DOI:10.16652/j.issn.1004-373x.2024.24.025
基于改进YOLOv8的工厂行人检测算法
Factory pedestrian detection algorithm based on improved YOLOv8
摘要
Abstract
A factory pedestrian detection algorithm based on improved YOLOv8 is proposed to address the issues of insufficient accuracy,false positives,and missed detections in pedestrian detection algorithms in factories.The convolutional block attention mechanism(CBAM)module was introduced into the C2f module of YOLOv8 to help the backbone network focus on key features and suppress non key features,thereby improving the model's detection accuracy for occlusions and small targets.The convolutional neural network Conv module is replaced by the CoordConv module in the Neck network to make full use of the positioning ability of the module,so as to solving the positioning accuracy in object detection and improve the model's perception of spatial position.The Inner IoU loss function is used to replace the original CIoU loss function to improve the regression accuracy of object detection bounding boxes.A self-made pedestrian image dataset in a factory(3 600 images)are trained and tested.The experimental results show that in comparison with the basic YOLOv8 algorithm,the improved YOLOv8 algorithm can improve the average accuracy of the average mAP(mean average precision)and the frame rate FPS(frame rate per second)by 2.26%and 35.6 f/s,respectively,which can verify the improvement of the detection performance of the improved algorithm.关键词
行人检测/YOLOv8算法/深度学习/卷积块注意力机制模块(CBAM)/CoordConv/Inner-IoU损失函数Key words
pedestrian detection/YOLOv8 algorithm/deep learning/convolutional block attention mechanism module/CoordConv/Inner-IoU loss function分类
信息技术与安全科学引用本文复制引用
陈思涵,刘勇,何祥..基于改进YOLOv8的工厂行人检测算法[J].现代电子技术,2024,47(24):160-166,7.基金项目
四川省科技计划项目(22ZFSHFZ0001) （22ZFSHFZ0001）
