Bonding Defect Detection Based on Improved Single Shot MultiBox Detector

Abstract

To solve the problem of time-consuming and low efficiency in manual defect detection, this paper proposes a bonding defect detection algorithm based on improved Single Shot MultiBox Detector (SSD). DenseNet is used to replace VGG of the SSD algorithm to improve the detection effect of bonding defect. A novel feature fusion network is designed, in which dilated convolution is used to reduce the size of the low-level feature map, and it is fused with the high-level feature map, and then the Convolutional Block Attention Module (CBAM) attention mechanism is used to increase the ability to extract the features. Focal loss is used to control the ratio of positive and negative samples for training and suppress easily separable samples, so that the samples involved in training have better distribution and the model has better detection performance. Then, the defect data set is constructed and a comparison experiment is carried out. The results show that the mAP, Precision, and Recall of the improved SSD network are increased to 75.9 %, 77.3 %, and 75.6 %, respectively, which can better identify bonding defect.