轨道零部件级联缺陷检测算法

A Cascaded Defect Detection Algorithm for Track Components

轨道零部件缺陷检测长期面临缺陷样本稀缺的问题。在缺陷样本不足的情况下,采用现有的深度学习方法易使模型过拟合、泛化性能较差,难以满足实际轨道巡检要求。为此,文章提出一种融合实例分割、细粒度图像分类和传统图像处理的轨道零部件级联缺陷检测方法。其首先通过一种改进的快速实例分割网络来实现扣件和缺陷钢轨的定位;然后,通过分析分割掩码、定量比较扣件轮廓长度的方法,实现少样本条件下的扣件缺陷检测;同时,针对钢轨缺陷类别繁杂且细微、易混的问题,采用“实例分割+细粒度图像分类”的两阶段缺陷检测网络,并引进标签平滑技术以提升模型适应性和分类准确性。实验结果表明,在样本稀缺的情况下,采用所提出的缺陷检测方法,扣件断裂和丢失检测准确率为95.7%,钢轨缺陷检出率和缺陷细分类准确率分别为98%和95%,满足轨道零部件缺陷检测需求。

Track component defect detection has long faced the problem of scarcity of defect samples. In the case of insufficient defect samples, the existing deep learning methods are prone to overfitting the model, and the generalization performance is poor,making it difficult to meet the actual track inspection requirements. To this end, this paper proposes a cascaded track component defect detection method which combines instance segmentation, fine-grained image classification and traditional image processing.Firstly, an improved fast instance segmentation network is proposed to locate fasteners and defective rails. Secondly, in view of the scarcity of defective fastener samples, a method of mask analysis and quantitative comparison of fastener contour lengths is proposed to realize the low-sample condition. At the same time, in view of the problem that the categories of rail defects are complex and subtle and easy to mix, a two-stage defect detection network of "instance segmentation + fine-grained image classification" is proposed, and label smoothing technology is introduced to improve model adaptability and classification accuracy. Experimental results show that,in the case of scarce samples, the defect detection method proposed in this paper has an accuracy rate of 95.7% for fastener breakage and loss detection, and 98% and 95% for rail defect detection rate and defect sub-classification, respectively. It meets the demand for track component defect detection.