Defect Detection of Small Size Glass Panel Based on SE-RetinaNet
The defects in glass panels have the characteristics of low salience,small size,diverse forms,and few numbers.The existing advanced target detection algorithms are difficult to be competent for the quality inspection task of glass panels.Based on this,this paper proposes SE-RetinaNet,a small-size and low-sali-ency defect detection algorithm for glass panels.The algorithm introduces attention mechanism and self-at-tention mechanism to the Feature pyramid at both the top and the bottom,strengthen the network's ability to extract low-level features small size and the top-level network's ability to capture the characteristics of long-distance dependencies,and at the same time the introduction of the positioning in the end of the net-work subnet SE-Regression,by combining the advantages of residual block and Inception module,the accu-racy of positioning is strengthened and the network degradation is prevented.The experimental results show that the proposed algorithm can effectively detect the low saliency defects of various sizes in the glass pan-el,and its detection role is better than the existing classical target detection algorithms,which can play a good performance in the problem of glass panel defect detection.
small target detectionglass panel defect detectionFocal lossSE attention mechanismself-at-tention mechanism
NETL
NSTL
万方数据
