复杂环境下的冰箱金属表面缺陷检测

Defect detection of refrigerator metal surface in complex environment

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。

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.