MLMA-Net: multi-level multi-attentional learning for multi-label object detection in textile defect images
This work addresses multi-label object detection for textile defect inspection, an incremental improvement in a domain-specific application.
The paper tackled the problem of detecting multiple defects in textile images by proposing MLMA-Net, a multi-level multi-attentional network, which achieved better performance than state-of-the-art methods on a real-world industrial dataset.
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.