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Double-Attention YOLO: Vision Transformer Model Based on Image Processing Technology in Complex Environment of Transmission Line Connection Fittings and Rust Detection

Authors :
Zhiwei Song
Xinbo Huang
Chao Ji
Ye Zhang
Source :
Machines, Vol 10, Iss 11, p 1002 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Transmission line fittings have been exposed to complex environments for a long time. Due to the interference of haze and other environmental factors, it is often difficult for the camera to obtain high quality on-site images, and the traditional image processing technology and convolution neural networks find it difficult to effectively deal with the dense detection task of small targets with occlusion interference. Therefore, an image processing method based on an improved dark channel defogging algorithm, the fusion channel spatial attention mechanism, Vision Transformer, and the GhostNet model compression method is proposed in this paper. Based on the global receptive field of the saliency region capture and enhancement model, a small target detection network Double-attention YOLO for complex environments is constructed. The experimental results show that embedding a multi-head self-attention component into a convolutional neural network can help the model to better interpret the multi-scale global semantic information of images. In this way, the model learns more easily the distinguishable features in the image representation. Embedding an attention mechanism module can make the neural network pay more attention to the salient region of image. Dual attention fusion can balance the global and local characteristics of the model, to improve the performance of model detection.

Details

Language :
English
ISSN :
20751702
Volume :
10
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Machines
Publication Type :
Academic Journal
Accession number :
edsdoj.8d188f4f24bb4fff87148596c217005f
Document Type :
article
Full Text :
https://doi.org/10.3390/machines10111002