High-resolution cross-scale transformer: A deep learning model for bolt loosening detection based on monocular vision measurement

Abstract

The reliability of bolt connections significantly impacts the operational state and lifespan of industrial equipment. Vision-based noncontact methods exhibit high efficiency in bolt loosening detection. However, limited image features hinder measurement accuracy. To improve bolt loosening detection performance, this paper proposes a novel deep learning backbone, the high-resolution cross-scale transformer, to extract high precision keypoints for bolt three-dimensional model construction. Simultaneously, a monocular vision measurement model is established to get the bolt exposed length and evaluate the connection loosening state. The proposed backbone hybridizes the advantages of high-resolution architecture and transformer, realizing global information aggregation and fine-grained image details. A simplified module, dual-scale multi-head self-attention, is designed to reduce the computational redundancy caused by the implementation of high-resolution multi-branch architecture. In the experiment section, the high-resolution cross-scale transformer outperforms other keypoint detection baselines, achieving the top one performance with 91.6 average precision and 84.9 average recall. The monocular vision measurement model realizes a 0.053 mm error with a 0.028 mm standard deviation, satisfying the industrial implementation requirement. Additionally, the model is tested on different industrial situations and an additional outside dataset, indicating the model's robustness and actual environment adaptability.