A Compact CNN for Weakly Supervised Textured Surface Anomaly Detection

Photo by Remy Gieling on Unsplash

In this article, I’ll be discussing a paper [1] that proposes a compact convolutional neural network (CNN) for detecting anomalies/defects from weakly/coarsely labelled data. The article is organized as follows.

• Introduction
• Methodology
  ◦ Segmentation Network
  ◦ Classification Network
  ◦ Architectural Specifications
• Experimental Setup
  ◦ Loss functions
  ◦ Optimizer
  ◦ Dataset
  ◦ Training setup
• Results
  ◦ Quantitative Results
  ◦ Qualitative Results
• Discussion
  ◦ Classification Network Performance
  ◦ Segmentation Network Performance
• Suggested Modifications
• Conclusion
• GitHub code
• References

Introduction

Surface defect detection is an essential task in the manufacturing process to ensure that the end product meets the quality standards and works in the way it is intended. A common property of these surface defects is that their visual texture is inherently different from the defect-free surface [2]. That is why visual inspection systems are used for detecting these defects. The manual task of looking at objects and finding those anomalies is difficult and tedious. The…