An end-to-end steel surface defect detection approach via fusing multiple hierarchical features
journal contributionposted on 2020-05-06, 10:14 authored by Yu He, Kechen Song, Qinggang MengQinggang Meng, Yunhui Yan
A complete defect detection task aims to achieve the specific class and precise location of each defect in an image, which makes it still challenging for applying this task in practice. The defect detection is a composite task of classification and location, leading to related methods is often hard to take into account the accuracy of both. The implementation of defect detection depends on a special detection data set that contains expensive manual annotations. In this paper, we proposed a novel defect detection system based on deep learning and focused on a practical industrial application: steel plate defect inspection. In order to achieve strong classification ability, this system employs a baseline convolution neural network (CNN) to generate feature maps at each stage, and then the proposed multilevel feature fusion network (MFN) combines multiple hierarchical features into one feature, which can include more location details of defects. Based on these multilevel features, a region proposal network (RPN) is adopted to generate regions of interest (ROIs). For each ROI, a detector, consisting of a classifier and a bounding box regressor, produces the final detection results. Finally, we set up a defect detection data set NEU-DET for training and evaluating our method. On the NEU-DET, our method achieves 74.8/82.3 mAP with baseline networks ResNet34/50 by using 300 proposals. In addition, by using only 50 proposals, our method can detect at 20 ft/s on a single GPU and reach 92% of the above performance, hence the potential for real-time detection.
National Natural Science Foundation of China under Grant 51805078 and Grant 51374063
National Key Research and Development Program of China under Grant 2017YFB0304200
Fundamental Research Funds for the Central Universities under Grant N170304014 and Grant N150308001
China Scholarship Council under Grant 201806085007
- Computer Science