Improved YOLOv3 Algorithm For Detection of Metal Surface Defect
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摘要: 针对现有的金属表面缺陷检测方法存在着检测效率低、适用范围受限、处理步骤繁琐等缺陷，提出了基于改进型YOLOv3算法的实时缺陷检测方法。该方法将采集到的图片分为N×N个格子，每个格子用来检测缺陷的中心点是否在格子中，利用特征金字塔与残差层融合特征的方式对图片中的缺陷进行定位，得到多个缺陷的边界框，使用非极大抑制的方法筛选出得分最高的边界框。为了提高检测效果，在输入端对图像进行直方图均衡化，并基于缺陷权重优化了算法中的损失函数以提高缺陷分类的准确性。最后，利用改进型YOLOv3算法对钢板表面的压痕与划痕进行了实验检测，结果显示该方法可以快速、准确检测出钢材表面的压痕与划痕，精度分别为92%和90%。Abstract: In view of the existing detection methods of steel plate defect, which have the low efficiency of detection, limited scope of application, cumbersome operation steps and so on, a real-time detection method of defect based on an improved YOLOv3(You Only Look Once version 3) algorithm is proposed. The method divides the captured images into N×N grids, each grid is used to detect whether there is a center point of defect in the grid, and through multi-scale detection and residual layer fusion feature to locate the defects in the image, get the boundary boxes of multiple defects, and finally use the non maximum inhibition method to screen the highest score boundary boxes. In order to improve the detection effect, the histogram equalization image is used in the input, and the weight based loss function is used, which effectively improves the problem that the model mistakenly judges scratches as indentation. Experimental detection of indentations and scratches on steel plate surfaces with an improved YOLOv3 algorithm. The results show that the method can effectively detect the indentation and scratch on the steel surface, with the accuracy of 92% and 90% respectively.
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表 2 检测结果统计
压痕 划痕 精确率P 召回率R 精确率P 召回率R 优化前 88.1% 94.9% 90.5% 95.0% 优化后 89.2% 84.6% 91.9% 91.9% 
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