关键词: 小目标, IoU补偿, 不平衡加权损失, 正样本数量增强

Abstract: During the inspection process of industrial instrument LCD displays,pixel defects are difficult to detect due to its small pixel size.Traditional computer vision methods are sensitive to environmental changes and require manual setting of parameters.In response to the above problems,this paper designs an LCD screen defect detection algorithm based on deep learning,which can identify pixel-level pixel defects on the LCD screen under lower computing power.The main work includes:(1)Aiming at the problem of the small number of positive samples in the sample assigner process of positive and negative samples for small-sized targets,an adaptive positive samples enhancement method for targets of different sizes is proposed.(2)Aiming at the problem of difficulty in small-sized targets training caused by small IoU of positive samples,an adaptive positive sample IoU compensation weighting method is proposed.(3)Aiming at the problem that small data sets are sensitive to hyperparameters in the loss function,a positive and negative cross-entropy imbalance weight classification loss function is designed.(4)In order to solve the pro-blem that detailed features of small-sized targetare difficult to extract,frequency channel attention is introduced in the backbone network to enhance the ability to extract detailed features of small targets.Experiments show that compared with the baselinecomparison model YOLOV8,the mAP_s reaches 63.3%,which is 3.7% higher than the baseline.The mAP_s for pixel defects reaches 78.85%,which improves 4.5%.Meanwhile,the recall rate of pixel defects reaches 99.8%.The mAP_s for dust detection targets reaches 47.8%,improves 3%.These fully verify the effectiveness of the proposed algorithm.

Key words: Minor targets, IoU compensation, Imbalanced weighted loss, Positive samples enhancement