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

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 ne...
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Autor*in:	Zhiwei Song [verfasserIn] Xinbo Huang [verfasserIn] Chao Ji [verfasserIn] Ye Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	transmission line connection fittings multi-scale target detection Vision Transformer image defogging technology attention mechanism model compression and optimization

Übergeordnetes Werk:	In: Machines - MDPI AG, 2013, 10(2022), 11, p 1002
Übergeordnetes Werk:	volume:10 ; year:2022 ; number:11, p 1002

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/machines10111002

Katalog-ID:	DOAJ083664726

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allfieldsSound	10.3390/machines10111002 doi (DE-627)DOAJ083664726 (DE-599)DOAJ8d188f4f24bb4fff87148596c217005f DE-627 ger DE-627 rakwb eng TJ1-1570 Zhiwei Song verfasserin aut Double-Attention YOLO: Vision Transformer Model Based on Image Processing Technology in Complex Environment of Transmission Line Connection Fittings and Rust Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. transmission line connection fittings multi-scale target detection Vision Transformer image defogging technology attention mechanism model compression and optimization Mechanical engineering and machinery Xinbo Huang verfasserin aut Chao Ji verfasserin aut Ye Zhang verfasserin aut In Machines MDPI AG, 2013 10(2022), 11, p 1002 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:10 year:2022 number:11, p 1002 https://doi.org/10.3390/machines10111002 kostenfrei https://doaj.org/article/8d188f4f24bb4fff87148596c217005f kostenfrei https://www.mdpi.com/2075-1702/10/11/1002 kostenfrei https://doaj.org/toc/2075-1702 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 11, p 1002
language	English
source	In Machines 10(2022), 11, p 1002 volume:10 year:2022 number:11, p 1002
sourceStr	In Machines 10(2022), 11, p 1002 volume:10 year:2022 number:11, p 1002
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	transmission line connection fittings multi-scale target detection Vision Transformer image defogging technology attention mechanism model compression and optimization Mechanical engineering and machinery
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container_title	Machines
authorswithroles_txt_mv	Zhiwei Song @@aut@@ Xinbo Huang @@aut@@ Chao Ji @@aut@@ Ye Zhang @@aut@@
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callnumber-first	T - Technology
author	Zhiwei Song
spellingShingle	Zhiwei Song misc TJ1-1570 misc transmission line connection fittings misc multi-scale target detection misc Vision Transformer misc image defogging technology misc attention mechanism misc model compression and optimization misc Mechanical engineering and machinery Double-Attention YOLO: Vision Transformer Model Based on Image Processing Technology in Complex Environment of Transmission Line Connection Fittings and Rust Detection
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topic_title	TJ1-1570 Double-Attention YOLO: Vision Transformer Model Based on Image Processing Technology in Complex Environment of Transmission Line Connection Fittings and Rust Detection transmission line connection fittings multi-scale target detection Vision Transformer image defogging technology attention mechanism model compression and optimization
topic	misc TJ1-1570 misc transmission line connection fittings misc multi-scale target detection misc Vision Transformer misc image defogging technology misc attention mechanism misc model compression and optimization misc Mechanical engineering and machinery
topic_unstemmed	misc TJ1-1570 misc transmission line connection fittings misc multi-scale target detection misc Vision Transformer misc image defogging technology misc attention mechanism misc model compression and optimization misc Mechanical engineering and machinery
topic_browse	misc TJ1-1570 misc transmission line connection fittings misc multi-scale target detection misc Vision Transformer misc image defogging technology misc attention mechanism misc model compression and optimization misc Mechanical engineering and machinery
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title	Double-Attention YOLO: Vision Transformer Model Based on Image Processing Technology in Complex Environment of Transmission Line Connection Fittings and Rust Detection
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title_full	Double-Attention YOLO: Vision Transformer Model Based on Image Processing Technology in Complex Environment of Transmission Line Connection Fittings and Rust Detection
author_sort	Zhiwei Song
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title_sort	double-attention yolo: vision transformer model based on image processing technology in complex environment of transmission line connection fittings and rust detection
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title_auth	Double-Attention YOLO: Vision Transformer Model Based on Image Processing Technology in Complex Environment of Transmission Line Connection Fittings and Rust Detection
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Double-Attention YOLO: Vision Transformer Model Based on Image Processing Technology in Complex Environment of Transmission Line Connection Fittings and Rust Detection
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