基于Faster-RCNN的钢带缺陷检测方法

目前实际工业生产中的钢带缺陷检测任务存在数据难以收集、缺陷识别效果较差等问题,为此提出一种基于Faster-RCNN的钢带缺陷检测模型FRDNet.通过k-means聚类获得锚框参数,使生成框更符合目标缺陷类别比例,提高缺陷检测精度;同时利用模型迁移的方式微调网络结构,使钢带缺陷检测模型更快地适应目标缺陷任务.该方法有...

Defect detection method of steel strip based on Faster-RCNN

At present, there are some problems in the steel strip defect detection task in the actual industrial production, such as difficult data collection and poor defect recognition. Therefore, a kind of steel strip defect detection algorithm based on Faster-RCNN—FRDNet is proposed. The anchor box parameters are obtained by k-means clustering, which makes the generated box more in line with the proportion of target defect categories and improves the accuracy of defect detection. At the same time, the network structure is fine-tuned by model migration, so that the steel strip defect detection model can better adapt to the target defect task, effectively solve the problem of less target data on the surface defects of the target steel strip, and enhance the generalization of the model. Experimental results show that the mAP of the model on the GC10-DET steel strip defect data set reaches 67.6%, which is 4.9% higher than that of the original model results, and the detection speed is 27.2FPS, meeting the requirements of the detection task.