安徽大学学报（自然科学版）2024，Vol.48Issue(6)：55-62,8.DOI:10.3969/j.issn.1000-2162.2024.06.008
面向堆叠薄型钢板计数的改进YOLOv5网络
An improved YOLOv5 network for stacked thin steel plate counting
摘要
Abstract
To address the issues of labor-intensive manual counting and high error rates in stacked thin steel plate detection,an improved YOLOv5(you only look once,version 5)network was proposed.SPD-Conv(symmetric positive definite convolution)was employed to replace CSP-Darknet(cross stage partial dlarknet),and the efficient channel attention(ECA)mechanism was integrated into feature extraction.Additionally,BiFPN(bi-directional feature pyramid network)was used to replace the PANet(path aggregation network)in YOLOv5,and an HIOU_Loc(height intersection over union location)method for redundant bounding box prediction was introduced.The experimental results demonstrated that,compared to three other networks,the proposed network achieved the highest accuracy(ACC)and mean average precision(mAP).Furthermore,the proposed network was adaptable to various detection environments and exhibited excellent detection performance.关键词
堆叠薄型钢板计数/YOLOv5/SPD-Conv/注意力机制/BiFPNKey words
stacked thin steel plate counting/YOLOv5/SPD-Conv/attention mechanism/BiFPN分类
信息技术与安全科学引用本文复制引用
侯维岩,刘忠英,郭怀远,翟鹏飞..面向堆叠薄型钢板计数的改进YOLOv5网络[J].安徽大学学报（自然科学版）,2024,48(6):55-62,8.基金项目
国家自然科学基金重大研究计划项目(9206710030) （9206710030）
