Abstract: Aiming to the problems of a wide variety of conveyor belt defects,a small proportion of defect feature pixels,and the low detection accuracy of traditional algorithms,random affine transformation is used to expand the sample dataset.The influence of the correlation between each channel and its contribution value on the model feature extraction is analyzed,and a channel correlation weighted attention mechanism is proposed.The correlation degree and contribution weight of each channel are calculated by correlation convolution and full connection,and the proportion of corresponding channel information is adjusted to improve the detection accuracy of the model.The influence of upsampling and convolution block on the size of the output feature map is analyzed.The original feature pyramid feature convolution block and upsampling structure are improved to enhance the feature extraction and defect detection ability of the algorithm for small targets.Finally,the test is conducted on the conveyor belt defect data set.The results show that the improved algorithm model can effectively identify the typical defect features such as foreign body insertion,breakage,and tearing of the conveyor belt.The recognition precision can reach 99.7%,the recall rate is increased to 99.5%,and the mean average precision is 99.5%.

Key words: Belt defect detection, Deep learning, Channel association weighting, Small target detection layer