复杂环境下的冰箱金属表面缺陷检测

为了提升冰箱金属表面的缺陷检测效率，从而应对复杂的生产情况，提出了Metal-YOLOv3模型。使用随机参数变换，将缺陷数据进行了数百倍的扩充，改变原有YOLOv3模型的损失函数，引入了基于完整交并比（CIoU）所设计的CIoU损失函数，用缺陷的分布特性来降低非极大值抑制算法的阈值，并基于K均值聚类算法计算出更适合数据特点的先验框（anchors）值以提升检测精度。在一系列的实验后，发现Metal-YOLOv3模型在检测速度上远胜于主流的区域卷积神经网络（R-CNN）模型，每秒传输帧数（FPS）达到7.59，是Faster R-CNN的14倍，而且平均精确度（AP）也达到了88.96%，比Faster R-CNN高11.33个百分点，说明所提模型同时具备良好的鲁棒性与泛化性能。可见该方法具备有效性，能实际应用于金属制品的生产。

Defect detection of refrigerator metal surface in complex environment

In order to improve the efficiency of detecting defects on the metal surface of refrigerators and to deal with complex production situations,the Metal-YOLOv3 model was proposed.Using random parameter transformation,the defect data was expanded hundreds of times;the loss function of the original YOLOv3(You Only Look Once version 3)model was changed,and the Complete Intersection-over-Union(CIoU)loss function based on CIoU was introduced;the threshold of non-maximum suppression algorithm was reduced by using the distribution characteristics of defects;and the anchor value that is more suitable for the data characteristics was calculated based on K-means clustering algorithm,so as to improve the detection accuracy.After a series of experiments,it is found that the Metal-YOLOv3 model is far better than the mainstream Regional Convolutional Neural Network(R-CNN)model in term of detection speed with the Frames Per Second(FPS)reached 7.59,which is 14 times faster than that of Faster R-CNN,and has the Average Precision(AP)reached 88.96%,which is 11.33 percentage points higher than Faster R-CNN,showing the good robustness and generalization performance of the proposed model.It can be seen that this method is effective and can be practically applied to the production of metal products.