摘要
针对钢材表面缺陷区域小目标居多,现有大部分方法无法均衡检测精度和速度的问题,提出一种基于YOLOv7-tiny的钢材表面缺陷检测算法(GBS-YOLOv7t)。该方法一是设计GAC-FPN网络,采用渐进和跨层的方式充分融合目标语义信息,以改善传统特征金字塔中存在限制信息流问题;二是嵌入双层路由注意力模块,使模型具备动态查询和感知稀疏性能力,以提高对小目标的检测精度;三是引入SIoU损失函数,提升模型训练和推理能力,增强网络鲁棒性。最后在公共数据集NEU-DET进行实验验证,mAP和精确度分别为72.9%和69.9%,相较于YOLOv7-tiny原模型分别提升4.2%和8.5%;FPS达到104.1帧,具有较强实时性;与其他检测算法相比,GBS-YOLOv7t算法对钢材表面区域小目标的检测更有效,实验表明改进后的算法能够更好地均衡检测精度和速度。
Given that small targets are predominant in the steel surface defect areas,most existing methods cannot balance the trade-off between detection accuracy and speed.In this paper,we propose a steel surface defect detection algorithm based on YOLOv7-tiny(GBS-YOLOv7t).Firstly,we design the GAC-FPN network to fully integrate the target semantic information progressively and across layers,aiming to address the limited information flow issue in traditional feature pyramids.Secondly,we embed a bi-level routing attention(BRA)module to endow the model with dynamic query and sparse perception capabilities,thus enhancing the detection accuracy of small targets.Thirdly,we introduce the SIoU loss function to improve the training and inference capabilities of the model,and to enhance the network robustness.Experimental validation on the public dataset NEU-DET demonstrates an mAP of 72.9%and a precision of 69.9%for GBS-YOLOv7t,achieving improvements of 4.2%and 8.5%,respectively,over the original YOLOv7-tiny model.The FPS reaches 104.1 frames,indicating strong real-time performance.Compared to other detection algorithms,GBS-YOLOv7t is more effective in detecting small targets in steel surface areas,with experimental results showing that the improved algorithm better balances the detection accuracy and speed.
作者
梁礼明
龙鹏威
卢宝贺
李仁杰
Liang Liming;Long Pengwei;Lu Baohe;Li Renjie(School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China)
出处
《光电工程》
CAS
CSCD
北大核心
2024年第5期54-65,共12页
Opto-Electronic Engineering
基金
国家自然科学基金资助项目(51365017,61463018)
江西省自然科学基金面上项目(20192BAB205084)
江西省教育厅科学技术研究重点项目(GJJ170491)
江西省研究生创新专项资金项目(YC2022-S676)。
