Lightweight highway debris detection algorithm based on improved YOLOv8n

Wenjing Zheng; Haotian Sun; Tianci Yang; Jun Li; Fei Lin; Xuehan Zheng; He Gao

doi:10.1117/12.3054582

20 December 2024 Lightweight highway debris detection algorithm based on improved YOLOv8n

Wenjing Zheng, Haotian Sun, Tianci Yang, Jun Li, Fei Lin, Xuehan Zheng, He Gao

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134211L (2024) https://doi.org/10.1117/12.3054582
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

To address the issues of poor detection performance, high false positive and missed detection rates, and low real-time capability in highway debris detection algorithms, a streamlined detection algorithm leveraging an enhanced YOLOv8n model is introduced. Firstly, the backbone HGnetv2 network from the Real-Time Detection Transformer model is integrated to optimize the detection functionality of small objects and debris in complex scenarios. Secondly, the improved CSP-PartialConv structure is used to replace C2f in the neck network, reducing parameter volume and computational demand. Additionally, the model's multi-scale feature fusion capability is improved by incorporating a weighted bidirectional feature pyramid network. Finally, the WIoU loss function is utilized for replace CIoU, optimizing the network's bounding box loss, and enhancing the model's convergence rate and accuracy in regression. The trial findings indicate that the improved model presented in this paper improves accuracy by 0.5% over the original model, while also reducing the parameter count by 24.1%, computational cost by 11%, and model size by 22.3%. This demonstrates a notable lightweight effect and improved detection performance, providing effective technical support for highway safety management.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenjing Zheng, Haotian Sun, Tianci Yang, Jun Li, Fei Lin, Xuehan Zheng, and He Gao "Lightweight highway debris detection algorithm based on improved YOLOv8n", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134211L (20 December 2024); https://doi.org/10.1117/12.3054582

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KEYWORDS

Object detection

Detection and tracking algorithms

Performance modeling

Feature extraction

Neck

Education and training

Feature fusion

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