改进YOLOv7-tiny的轻量化大型铸件焊缝缺陷检测
Improved YOLOv7-tiny Lightweight Large Casting Weld Defect Detection
穆春阳 1李闯 2马行 3刘永鹿 2杨科 2刘宝成2
作者信息
- 1. 北方民族大学宁夏智能信息与大数据处理重点实验室,银川 750021;北方民族大学机电工程学院,银川 750021
- 2. 北方民族大学电气信息工程学院,银川 750021
- 3. 北方民族大学电气信息工程学院,银川 750021;北方民族大学宁夏智能信息与大数据处理重点实验室,银川 750021
- 折叠
摘要
针对目前焊缝缺陷数据集少,检测环境恶劣,人为识别困难等问题,提出了一种改进的YOLOv7-tiny算法.由于检测物体缺陷形状不规则,采用可变形卷积能够学习到更加丰富的特征信息和感知到物体的细节信息,增强了模型的表达能力和泛化能力;为了在提高焊缝缺陷检测速度的同时,不降低准确率,满足工厂实时性的要求,提出了一种融合轻量化卷积和注意力机制的ELAN-PCS 网络结构;为了解决中小目标检测困难,很容易出现漏检的情况,引入了通道注意力机制.实验结果表明,与原 YOLOv7-tiny 相比,改进模型在大型铸件焊缝缺陷数据集上 mAP(0.5)提升1.8%、mAP(0.5～0.95)提升6.8%,模型参数量下降1.9 M.
Abstract
An improved YOLOv7-tiny algorithm is proposed to solve the problems of few weld defect data sets,poor detection environment and difficult artificial recognition.Due to the irregular shape of the detec-ted object defect,the deformable convolution can learn more abundant feature information and perceive the detail information of the object,which enhances the expression ability and generalization ability of the mod-el.In order to improve the detection speed of weld defects without reducing the accuracy and meet the real-time requirements of the factory,an ELAN-PCS network structure combining lightweight convolution and attention mechanism is proposed.In order to solve the difficulty of small and medium target detection,it is easy to miss detection,and the channel attention mechanism is introduced.The experimental results show that compared with the original YOLOv7-tiny,the improved model increases mAP(0.5)by 1.8%and mAP(0.5～0.95)by 6.8%on the large-scale casting weld defect dataset,and the number of model pa-rameters decreases by 1.9 M.
关键词
轻量化/缺陷检测/YOLOv7-tiny/注意力机制/可变形卷积Key words
lightweight/defect detection/YOLOv7-tiny/attention mechanism/deformable convolution引用本文复制引用
基金项目
宁夏回族自治区重点研发计划项目(2021BEE03002)
银川市科技创新项目(2022GX04)
自治区科技创新领军人才培养工程项目(2021GKLRLX08)
北方民族大学研究生创新项目(YCX22121)
出版年
2024
NETL
NSTL
万方数据