The design of lightweight vehicle detection model based on improved YOLOv5

Wenyu Jiang; Jiayan Wen; Guangming Xie; Kene Li

doi:10.1117/12.3001012

9 August 2023 The design of lightweight vehicle detection model based on improved YOLOv5

Wenyu Jiang, Jiayan Wen, Guangming Xie, Kene Li

Proceedings Volume 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023); 127820P (2023) https://doi.org/10.1117/12.3001012
Event: Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Convolutional neural network-based target detection algorithms are widely used in vehicle detection due to their high speed and accuracy. However, existing algorithms are characterized by large computational volumes, complex network structures, and severe resource constraints. They make them difficult to be ported to mobile platforms and embedded devices. Therefore, the structure of the relevant target detection algorithm needs to be optimized to enable wider deployment of the algorithm. To address the problems mentioned earlier, a YOLOv5SCB lightweight target detection network model is proposed. In the presented model, Shufflenetv2 and CA module are introduced into the backbone network to reduce the complexity of the network model and improve the detection accuracy of the model. Furthermore, BiFPN is integrated into the neck network to improve the efficiency of network feature fusion and enhance the ability of network feature expression. The experimental data show that compared with the original YOLOv5, the model parameters of the proposed YOLOv5SCB are reduced by 62.4% and the overall detection accuracy is improved by 1.1%.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenyu Jiang, Jiayan Wen, Guangming Xie, and Kene Li "The design of lightweight vehicle detection model based on improved YOLOv5", Proc. SPIE 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 127820P (9 August 2023); https://doi.org/10.1117/12.3001012

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KEYWORDS

Feature fusion

Convolution

Education and training

Data modeling

Network architectures

Autonomous vehicles

Neck

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