基于改进YOLOV8的图像液晶显示屏像素缺陷检测算法

Graphical LCD Pixel Defect Detection Algorithm Based on Improved YOLOV8

在工业仪表液晶显示屏检测过程中,由于显示屏像素尺寸较小,像素缺陷难以被检测。传统的计算机视觉方法对环境变化敏感,需要手动设置参数。针对上述问题,设计了一种基于深度学习的液晶屏缺陷检测算法,其能够在较低的算力条件下识别液晶屏的像素级别像素缺陷。主要工作包括:(1)针对小尺寸目标正负样本匹配过程中正样本数量较少的问题,提出了一种不同尺寸目标的自适应正样本数量增强方法;(2)针对小尺寸目标正样本IoU小导致训练困难的问题,提出了一种自适应正样本IoU补偿加权方法;(3)针对小数据集对超参数敏感的问题,设计了一种正负交叉熵不平衡权重分类损失函数;(4)针对小尺寸目标细节特征提取困难的问题,在主干网络中引入了频域通道注意力,强化了小目标的细节特征提取能力。实验结果表明,相较于基线模型YOLOV8,此算法的小尺寸检测目标的mAP_s达到63.3%,提高了3.7%。其中,小尺寸像素缺陷的mAP_s达到78.8%,提升了4.5%;灰尘杂质检测目标的mAP_s达到47.8%,提升了3%;像素缺陷召回率达到99.8%。以上结果充分验证了算法的有效性。

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.