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Tp-yolov8: a lightweight and accurate model for traffic accident recognition

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Abstract

Traffic accident detection is an important part of road safety, impacting the lives of those involved and others on the road. Using surveillance cameras on traffic poles to detect accidents poses unique challenges, such as incomplete dataset categories, small-sized detection objects, and the need for lightweight models. Current traffic accident recognition algorithms, while effective in detection, often require extensive resources, making deployment on edge devices difficult. This paper proposes a more accurate and lightweight traffic accident recognition model based on YOLOv8, optimized for traffic pole monitoring and deployment on edge devices. To improve small object detection, we made improvements to the neck. We modified the neck by adding a detection layer for small-sized objects using large-scale feature maps, along with a dedicated small object detection head (SODL-SODH). Additionally, we design a lightweight cross-scale feature fusion module (LCSFFM) to optimize the PAN-FPN structure, reducing model parameters and computational complexity while enhancing small-target detection. In the downsampling layer, we incorporate the squeeze-excited aggregate spatial attention module (SEASAM) into the C2F module to help the network focus on essential image information, with minimal impact on model parameters and computational complexity. To address dataset limitations, we built the traffic accident-type (TAT) dataset for training and evaluation, and validated it against other advanced methods. Experimental results show that our model outperforms the baseline on the TAT dataset, improving the mAP0.5 by 1% and reducing parameters by 25.9%. On the BDD-IW dataset, our TP-YOLOv8s outperforms other methods in terms of accuracy. Compared with the best other methods, it improves the mAP0.5 index by 1.4% and reduces the number of parameters by 84.1%.

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Data Availability

The datasets used in the study are from the website and can be downloaded through the following links: BDD-IW dataset (https://github.com/ZhaoHe1023/Improved-YOLOv4), TAT dataset (https://github.com/Ningdashuai/TAT).

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Funding

This research was funded by the Key R&D projects of Xinjiang Uygur Autonomous Region, grant number 2022B01006.

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Author notes

  1. Z. Ning, T. Zhang: These authors contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Technology, Xinjiang University, Huarui Street, Urumqi, 830000, Xinjiang, China

    Zhaole Ning, Xin Li, Aiying Wu & Gang Shi

  2. Faculty of Science, The University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia

    Tianze Zhang

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  1. Zhaole Ning
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Contributions

Z.N. and T.Z. conceptualized the study; Z.N. curated and processed the data; Z.N. and T.Z. conducted formal analysis; G.S. acquired funding, provided resources, participated in the investigation, and supervised the project; Z.N. designed the methodology; A.W. and X.L. managed the project’s progress and coordination; Z.N. and X.L. validated the results and created visualizations; Z.N. wrote the main manuscript text; A.W. and T.Z. reviewed and edited the manuscript.

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Correspondence to Gang Shi.

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Ning, Z., Zhang, T., Li, X. et al. Tp-yolov8: a lightweight and accurate model for traffic accident recognition. J Supercomput 81, 622 (2025). https://doi.org/10.1007/s11227-025-07129-6

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