To accurately detect small defects in urine test strips, the SK-FMYOLOV3 defect detection algorithm is proposed. First, the prediction box clustering algorithm of YOLOV3 is improved. The fuzzy C-means clustering algorithm is used to generate the initial clustering centers, and then, the clustering center is passed to the K-means algorithm to cluster the prediction boxes. To better detect smaller defects, the YOLOV3 feature map fusion is increased from the original three-scale prediction to a four-scale prediction. At the same time, 23 convolutional layers of size in the YOLOV3 network are replaced with SkNet structures, so that different feature maps can independently select different convolution kernels for training, improving the accuracy of defect classification. We collected and enhanced urine test strip images in industrial production and labeled the small defects in the images. A total of 11634 image sets were used for training and testing. The experimental results show that the algorithm can obtain an anchor frame with an average cross ratio of 86.57, while the accuracy rate and recall rate of nonconforming products are 96.8 and 94.5, respectively. The algorithm can also accurately identify the category of defects in nonconforming products.

中文翻译：

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SK-FMYOLOV3：尿液试纸的新型检测方法

为了准确检测尿液试纸中的小缺陷，提出了SK-FMYOLOV3缺陷检测算法。首先，对YOLOV3的预测框聚类算法进行了改进。采用模糊C均值聚类算法生成初始聚类中心，然后将聚类中心传递给K-means算法对预测框进行聚类。为了更好地检测较小的缺陷，将YOLOV3特征图融合从原来的三级预测增加到四级预测。同时，有23个卷积层在YOLOV3网络中，SkNet结构取代了它，因此不同的特征图可以独立选择不同的卷积核进行训练，从而提高了缺陷分类的准确性。我们在工业生产中收集并增强了尿液试纸图像，并标记了图像中的小缺陷。总共使用11634个图像集进行训练和测试。实验结果表明，该算法可获得平均交叉比为86.57的锚帧，不合格品的准确率和召回率分别为96.8和94.5。该算法还可以准确地识别不合格产品中的缺陷类别。