摘要
汽车零件对汽车外观、性能以及安全性都有重大影响。由于汽车零件数量大、体积小、对精度要求高,因此对零件检测的精度和速度都有一定的要求。本文利用图像处理技术,以SSD模型为基础,将其中的VGG模块用深度可分离卷积和线性瓶颈倒残差结构替换,并引入避免降维的局部跨通道交互有效的注意力机制ECA模块,在减少模型参数运算量的同时,适当增加通道以提高模型精度,并将注意力放在图像目标上,忽略背景带来的干扰,实现快速又准确的汽车零件缺陷检测。利用本文模型对上汽提供的汽车零件外壁缺陷进行检测,实验结果表明,模型大小仅为15.9 MB,mAP为94.64%,检测每张图片时间为0.013 s,满足汽车工业上的速度和精度的需求。对比性研究表明,本文模型检测精度和速度以及大小较其他目标检测算法VGG-SSD、MobileNetv2-SSD、YOLO v3等有一定的提高和改善。
Automobile parts have great influence on the appearance,performance and safety of automobile.Due to the large number of automobile parts,small volume and high accuracy requirements,there are certain requirements for the accuracy and speed of parts detection.Using image processing technology,based on SSD model,the VGG module is replaced by deep separable convolution and linear bottleneck inverse residual structure.An effective attention mechanism ECA module is introduced to avoid dimensionality reduction.At the same time the computational complexity of the model parameters is reduced,and the channel is increased to improve the accuracy of the model.And this paper focuses on the image target,ignores the interference of the background to achieve fast and accurate defect detection of automotive parts.In addition,the proposed model in this paper is used to detect the outer wall defects of automobile parts provided by SAIC.The experimental results show that the size of the model is only 15.9 MB,the mAPis 94.64%,and the detection time of each image is 0.013 s,which meets the requirements of speed and accuracy in the automotive industry.Compared with other target detection algorithms such as VGG-SSD、MobileNetv2-SSD and YOLO v3,the detection accuracy,speed and size of the proposed model are improved.
作者
金文倩
彭露露
朱媛媛
王笑梅
JIN Wen-qian;PENG Lu-lu;ZHU Yuan-yuan;WANG Xiao-mei(Department of Computer Science and Technology, Shanghai Normal University, Shanghai 200234, China)
出处
《计算机与现代化》
2022年第3期82-90,共9页
Computer and Modernization
基金
上海市自然科学基金资助项目(17ZR1419800)。
