Research on Intelligent Cognition Method of Missing status of Pins Based on attention mechanism

Li, Weitao; Guan, Huimin; Zhang, Qian; Wu, Gang; Ding, Meishuang

doi:10.1109/imcec51613.2021.9482019

Cited by 2 publications

(1 citation statement)

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“…This method effectively improves the accuracy of bolt defect classification. In [ 26 ], the authors used an attention mechanism to locate the possible regions of the bolt in the image and then combined it with a deconvolutional network to build a model to achieve accurate detection of the bolt. This is an attention-based mechanism for transmission tower bolt detection.…”

Section: Introductionmentioning

confidence: 99%

A New Bolt Defect Identification Method Incorporating Attention Mechanism and Wide Residual Networks

Liu

Zhao

Chen

et al. 2022

Sensors

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Bolts are important components on transmission lines, and the timely detection and exclusion of their abnormal conditions are imperative to ensure the stable operation of transmission lines. To accurately identify bolt defects, we propose a bolt defect identification method incorporating an attention mechanism and wide residual networks. Firstly, the spatial dimension of the feature map is compressed by the spatial compression network to obtain the global features of the channel dimension and enhance the attention of the network to the vital information in a weighted way. After that, the enhanced feature map is decomposed into two one-dimensional feature vectors by embedding a cooperative attention mechanism to establish long-term dependencies in one spatial direction and preserve precise location information in the other direction. During this process, the prior knowledge of the bolts is utilized to help the network extract critical feature information more accurately, thus improving the accuracy of recognition. The test results show that the bolt recognition accuracy of this method is improved to 94.57% compared with that before embedding the attention mechanism, which verifies the validity of the proposed method.

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Section: Introductionmentioning

confidence: 99%