End-to-end training of a two-stage neural network for defect detection

TL;DR

This paper introduces an end-to-end training approach for a two-stage neural network in surface defect detection, incorporating novel training extensions that enhance performance and reduce training time, achieving state-of-the-art results.

Contribution

The work presents a novel end-to-end training method with extensions like balanced loss contributions, frequency-of-use sampling, and weighted region-based masks, improving defect detection accuracy.

Findings

Achieved 100% detection rate on DAGM and KolektorSDD datasets.

Demonstrated state-of-the-art results on three defect detection datasets.

Ablation studies confirmed the effectiveness of each proposed extension.

Abstract

Segmentation-based, two-stage neural network has shown excellent results in the surface defect detection, enabling the network to learn from a relatively small number of samples. In this work, we introduce end-to-end training of the two-stage network together with several extensions to the training process, which reduce the amount of training time and improve the results on the surface defect detection tasks. To enable end-to-end training we carefully balance the contributions of both the segmentation and the classification loss throughout the learning. We adjust the gradient flow from the classification into the segmentation network in order to prevent the unstable features from corrupting the learning. As an additional extension to the learning, we propose frequency-of-use sampling scheme of negative samples to address the issue of over- and under-sampling of images during the training, while we employ the distance transform algorithm on the region-based segmentation masks as weights for positive pixels, giving greater importance to areas with higher probability of presence of defect without requiring a detailed annotation. We demonstrate the performance of the end-to-end training scheme and the proposed extensions on three defect detection datasets - DAGM, KolektorSDD and Severstal Steel defect dataset - where we show state-of-the-art results. On the DAGM and the KolektorSDD we demonstrate 100\% detection rate, therefore completely solving the datasets. Additional ablation study performed on all three datasets quantitatively demonstrates the contribution to the overall result improvements for each of the proposed extensions.