An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features

By Yu He, Kechen Song, Q. Meng, Yunhui Yan. IEEE Transactions on Instrumentation and Measurement, 2020

1,102 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/46d4a2b7ccf38b80b09574cf17544ff2297a8fbf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

In Computer Vision and Pattern Recognition

In Industrial and Manufacturing Engineering

Ming-Ming Cheng

Citations in the corpus

Concealed Object Detection (2021, background): “trast, artificial camouflage is usually used in art design/ gaming to hide information, occurs in products during the manufacturing process (so-called surface defects [42], defect detection [43], [44]), or appears in our daily life (e.”
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges (2020, methodology): “LeNet network structure can be used to detect defects in two situations: one is to design a complex multi-layer CNN structure, use different network structure to extra image content features, and complete end-to-end training to detect defects in images [56,83]; the other is to combine CNN with CRF m”