MLMA-Net: multi-level multi-attentional learning for multi-label object detection in textile defect images

• URL: http://arxiv.org/abs/2102.00376v1
• Date: Sun, 31 Jan 2021 04:50:40 GMT
• Title: MLMA-Net: multi-level multi-attentional learning for multi-label object detection in textile defect images
• Authors: Bing Wei (Student Member, IEEE), Kuangrong Hao (Member, IEEE), Lei Gao (Member, IEEE)
• Abstract summary: This paper proposes a multi-level, multi-attentional deep learning network (MLMA-Net) to detect multi-label defects in textile images. The results show that the network extracts more distinctive features and has better performance than the state-of-the-art approaches on the real-world industrial dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For the sake of recognizing and classifying textile defects, deep learning-based methods have been proposed and achieved remarkable success in single-label textile images. However, detecting multi-label defects in a textile image remains challenging due to the coexistence of multiple defects and small-size defects. To address these challenges, a multi-level, multi-attentional deep learning network (MLMA-Net) is proposed and built to 1) increase the feature representation ability to detect small-size defects; 2) generate a discriminative representation that maximizes the capability of attending the defect status, which leverages higher-resolution feature maps for multiple defects. Moreover, a multi-label object detection dataset (DHU-ML1000) in textile defect images is built to verify the performance of the proposed model. The results demonstrate that the network extracts more distinctive features and has better performance than the state-of-the-art approaches on the real-world industrial dataset.

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