%0 Journal Article
%T 基于改进型YOLOv5算法的偏振片缺陷识别研究<br>Research on Polarizer Defect Recognition Based on Improved YOLOv5 Algorithm
%A 贾晓斌
%A 罗柏文
%A 金祝红
%J Computer Science and Application
%P 113-123
%@ 2161-881X
%D 2025
%I Hans Publishing
%R 10.12677/csa.2025.154084
%X 偏振片作为TFT-LCD的关键部件之一&#65292;其表面缺陷严重影响液晶显示器的成像质量。为了实现偏振片缺陷智能化在线检测从而替代目前因人眼检测导致效率低的问题&#65292;提出了一种改进型YOLOv5检测算法&#65292;即在Backbone层增加了CBAM注意力机制&#65307;在Prediction层增加了一个新的输出层&#65307;将传统边框回归损失函数改为CIOU_Loss。通过缺陷样本测试实验表明&#65292;改进型算法尽管增加了一个输出层导致参数增加&#65292;且FPS略微降低&#65292;但mAP却提升了4个百分点&#65292;并且检测的最高置信度达到了0.93。故改进型YOLOv5算法增强了缺陷目标识别精度和准确度。<br>Polarizer is one of the key components of TFT-LCD. Its surface defects seriously affect the imaging quality of LCD. In order to realize the intelligent on-line detection of polarizer defects and replace the current problem of low efficiency caused by human eye detection, an improved YOLOv5 detection algorithm is proposed, that is, CBAM attention mechanism is added in the backbone layer; A new output layer is added in the prediction layer; The traditional border regression loss function is changed to CIOU_Loss. The defect sample test experiment shows that although the improved algorithm adds an output layer, resulting in an increase in parameters and a slight decrease in FPS, the map increases by 4 percentage points, and the highest confidence of detection reaches 0.93. Therefore, the improved YOLOv5 algorithm enhances the accuracy and accuracy of defect target recognition.
%K 缺陷检测&#65292
%K YOLOv5算法&#65292
%K CBAM注意力机制&#65292
%K 四尺度输出层<br>Defect Detection
%K YOLOv5 Algorithm
%K CBAM Attention Mechanism
%K Four Scale Output Layer
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=111303