Real-time detection of particleboard surface defects based on improved YOLOV5 target detection

Abstract Particleboard surface defect detection technology is of great significance to the automation of particleboard detection, but the current detection technology has disadvantages such as low accuracy and poor real-time performance. Therefore, this paper proposes an improved lightweight detecti...

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Autores principales:	Ziyu Zhao, Xiaoxia Yang, Yucheng Zhou, Qinqian Sun, Zhedong Ge, Dongfang Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cf87e4ec41e5488290b872ab9dbd4394
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spelling	oai:doaj.org-article:cf87e4ec41e5488290b872ab9dbd43942021-11-08T10:55:53ZReal-time detection of particleboard surface defects based on improved YOLOV5 target detection10.1038/s41598-021-01084-x2045-2322https://doaj.org/article/cf87e4ec41e5488290b872ab9dbd43942021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01084-xhttps://doaj.org/toc/2045-2322Abstract Particleboard surface defect detection technology is of great significance to the automation of particleboard detection, but the current detection technology has disadvantages such as low accuracy and poor real-time performance. Therefore, this paper proposes an improved lightweight detection method of You Only Live Once v5 (YOLOv5), namely PB-YOLOv5 (Particle Board-YOLOv5). Firstly, the gamma-ray transform method and the image difference method are combined to deal with the uneven illumination of the acquired images, so that the uneven illumination is well corrected. Secondly, Ghost Bottleneck lightweight deep convolution module is added to Backbone module and Neck module of YOLOv5 detection algorithm to reduce model volume. Thirdly, the SELayer module of attention mechanism is added into Backbone module. Finally, replace Conv in Neck module with depthwise convolution (DWConv) to compress network parameters. The experimental results show that the PB-YOLOv5 model proposed in this paper can accurately identify five types of defects on the particleboard surface: Bigshavings, SandLeakage, GlueSpot, Soft and OliPollution, and meet the real-time requirements. Specifically, recall, F1 score, mAP@.5, mAP@.5:.95 values of pB-Yolov5s model were 91.22%, 94.5%, 92.1%, 92.8% and 67.8%, respectively. The results of Soft defects were 92.8%, 97.9%, 95.3%, 99.0% and 81.7%, respectively. The detection of single image time of the model is only 0.031 s, and the weight size of the model is only 5.4 MB. Compared with the original YOLOv5s, YOLOv4, YOLOv3 and Faster RCNN, the PB-Yolov5s model has the fastest Detection of single image time. The Detection of single image time was accelerated by 34.0%, 55.1%, 64.4% and 87.9%, and the weight size of the model is compressed by 62.5%, 97.7%, 97.8% and 98.9%, respectively. The mAP value increased by 2.3%, 4.69%, 7.98% and 13.05%, respectively. The results show that the PB-YOLOV5 model proposed in this paper can realize the rapid and accurate detection of particleboard surface defects, and fully meet the requirements of lightweight embedded model.Ziyu ZhaoXiaoxia YangYucheng ZhouQinqian SunZhedong GeDongfang LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ziyu Zhao Xiaoxia Yang Yucheng Zhou Qinqian Sun Zhedong Ge Dongfang Liu Real-time detection of particleboard surface defects based on improved YOLOV5 target detection
description	Abstract Particleboard surface defect detection technology is of great significance to the automation of particleboard detection, but the current detection technology has disadvantages such as low accuracy and poor real-time performance. Therefore, this paper proposes an improved lightweight detection method of You Only Live Once v5 (YOLOv5), namely PB-YOLOv5 (Particle Board-YOLOv5). Firstly, the gamma-ray transform method and the image difference method are combined to deal with the uneven illumination of the acquired images, so that the uneven illumination is well corrected. Secondly, Ghost Bottleneck lightweight deep convolution module is added to Backbone module and Neck module of YOLOv5 detection algorithm to reduce model volume. Thirdly, the SELayer module of attention mechanism is added into Backbone module. Finally, replace Conv in Neck module with depthwise convolution (DWConv) to compress network parameters. The experimental results show that the PB-YOLOv5 model proposed in this paper can accurately identify five types of defects on the particleboard surface: Bigshavings, SandLeakage, GlueSpot, Soft and OliPollution, and meet the real-time requirements. Specifically, recall, F1 score, mAP@.5, mAP@.5:.95 values of pB-Yolov5s model were 91.22%, 94.5%, 92.1%, 92.8% and 67.8%, respectively. The results of Soft defects were 92.8%, 97.9%, 95.3%, 99.0% and 81.7%, respectively. The detection of single image time of the model is only 0.031 s, and the weight size of the model is only 5.4 MB. Compared with the original YOLOv5s, YOLOv4, YOLOv3 and Faster RCNN, the PB-Yolov5s model has the fastest Detection of single image time. The Detection of single image time was accelerated by 34.0%, 55.1%, 64.4% and 87.9%, and the weight size of the model is compressed by 62.5%, 97.7%, 97.8% and 98.9%, respectively. The mAP value increased by 2.3%, 4.69%, 7.98% and 13.05%, respectively. The results show that the PB-YOLOV5 model proposed in this paper can realize the rapid and accurate detection of particleboard surface defects, and fully meet the requirements of lightweight embedded model.
format	article
author	Ziyu Zhao Xiaoxia Yang Yucheng Zhou Qinqian Sun Zhedong Ge Dongfang Liu
author_facet	Ziyu Zhao Xiaoxia Yang Yucheng Zhou Qinqian Sun Zhedong Ge Dongfang Liu
author_sort	Ziyu Zhao
title	Real-time detection of particleboard surface defects based on improved YOLOV5 target detection
title_short	Real-time detection of particleboard surface defects based on improved YOLOV5 target detection
title_full	Real-time detection of particleboard surface defects based on improved YOLOV5 target detection
title_fullStr	Real-time detection of particleboard surface defects based on improved YOLOV5 target detection
title_full_unstemmed	Real-time detection of particleboard surface defects based on improved YOLOV5 target detection
title_sort	real-time detection of particleboard surface defects based on improved yolov5 target detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/cf87e4ec41e5488290b872ab9dbd4394
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_version_	1718442568918761472

Real-time detection of particleboard surface defects based on improved YOLOV5 target detection

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