Abstract: 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.

Key words: effective channel attention, depth separable convolution, inverted residual, defect detection, SSD(Single Shot Multibox Detector)