DefectNet: Toward Fast and Effective Defect Detection

The existing object detection algorithms based on the convolutional neural network (CNN) are always devoted to the detection of natural objects and have achieved admirable detection effects. At present, these detection algorithms have been applied to the detection of defect data. In fact, the detection of defect data is different from the detection of general natural object data. First, it has a large number of images without annotations (that is, normal images), and they each contain different background information. Second, its processing principles are fundamentally different from general object detection problems. Therefore, the application of a general object detection algorithm based on CNN may not be perfect in this problem. In this article, a novel defect detection network (DefectNet) is proposed to solve the problem of defect detection. It first uses a shared weight binary classification network to determine whether an image contains the targets and then uses the detection network to detect the targets. Theoretical deduction and experimental results fully confirm that it can effectively improve the detection speed and effect of the general object detection network based on CNN. (Our code is available at https://github.com/li-phone/DefectNet.git)

[1]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Guanglu Song,et al.  Revisiting the Sibling Head in Object Detector , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Nuno Vasconcelos,et al.  Cascade R-CNN: Delving Into High Quality Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[6]  Shibin Gao,et al.  Deep Architecture for High-Speed Railway Insulator Surface Defect Detection: Denoising Autoencoder With Multitask Learning , 2019, IEEE Transactions on Instrumentation and Measurement.

[7]  Guojun Wen,et al.  Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model , 2018, Sensors.

[8]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[9]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Jian Gao,et al.  Automatic surface defect detection for mobile phone screen glass based on machine vision , 2017, Appl. Soft Comput..

[12]  Kai Chen,et al.  MMDetection: Open MMLab Detection Toolbox and Benchmark , 2019, ArXiv.

[13]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[15]  Zhi Tang,et al.  CBNet: A Novel Composite Backbone Network Architecture for Object Detection , 2019, AAAI.

[16]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[17]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[18]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[19]  Qinggang Meng,et al.  An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features , 2020, IEEE Transactions on Instrumentation and Measurement.