Paper
11 September 2024 Licence plate detection in complex scenes based on improved YOLOv5
Shuting Liu, Yuqian Long, Teng Xu, Jidong Qu
Author Affiliations +
Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 132530Z (2024) https://doi.org/10.1117/12.3041112
Event: Fourth International Conference on Signal Image Processing and Communication, 2024, Xi'an, China
Abstract
To boost the mean average precision (mAP) of number plate detection in challenging scenarios characterized by uneven lighting, tilted number plates, varying angles, and other interfering elements, an enhanced target detection algorithm has been introduced by refining the YOLOv5 network architecture. Firstly, the lightweight network ShuffleNet v2 is introduced to replace the Backbone of YOLOv5, reducing the parameters of the YOLOv5 network and enhancing computational speed. Secondly, the Stemblock module replaces the header convolutional layer to enhance feature extraction quality and diversity. Finally, the comparative experiments demonstrate that the YOLOv5sand the original YOLOv5n models achieve an average detection accuracy of 94.8% for number plates, whereas the improved average accuracy rises to 99.5%, resulting in a noticeable increase in overall accuracy.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Shuting Liu, Yuqian Long, Teng Xu, and Jidong Qu "Licence plate detection in complex scenes based on improved YOLOv5", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 132530Z (11 September 2024); https://doi.org/10.1117/12.3041112
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KEYWORDS
Convolution

Head

Feature extraction

Detection and tracking algorithms

Education and training

Environmental sensing

Data modeling

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