Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection

2020/04/09 by Zheng Nie, Nie, Zheng, Jiachen Xu +3
Computer Science · Engineering · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #Currency Recognition and Detection #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection

paper · pdf · doi:10.48550/arxiv.2004.04822

openalex publication_date 2020/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Our works experimented DeepLabV3+ with different backbones on a large volume of steel images aiming to automatically detect different types of steel defects. Our methods applied random weighted augmentation to balance different defects types in the training set. And then applied DeeplabV3+ model three different backbones, ResNet, DenseNet and EfficientNet, on segmenting defection regions on the steel images. Based on experiments, we found that applying ResNet101 or EfficientNet as backbones could reach the best IoU scores on the test set, which is around 0.57, comparing with 0.325 for using DenseNet. Also, DeepLabV3+ model with ResNet101 as backbone has the fewest training time.

Citations

• Densely Connected Convolutional Networks
• Fully Convolutional Networks for Semantic Segmentation

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