Road damage detection algorithm based on GhostNet lightweight YoLov8

Shenglang Liao

doi:10.1117/12.3049529

8 November 2024 Road damage detection algorithm based on GhostNet lightweight YoLov8

Shenglang Liao

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134160K (2024) https://doi.org/10.1117/12.3049529
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

The detection of road injuries is crucial for preventing traffic accidents. Given the lack of lightweight in existing road damage detection algorithms, we propose an enhanced Yolov8 algorithm based on GhostNet. This approach effectively reduces the complexity of the network, in terms of both computing requirements and the number of parameters. Moreover, the incorporation of the CBAM attention mechanism module has led to notable enhancements in the accuracy and resilience of the detection process. Furthermore, the F-EIOU Loss is employed to further enhance speed of convergence and positioning accuracy of the model. Results on the RDD2022 dataset demonstrate that our method increases precision, recall, and mAP by 6.4%, 3.9%, and 5.4%, respectively. The parameter size is reduced by approximately 25%, thus confirming that our innovative improvements not only enhance detection accuracy significantly but also notably decrease quantity of parameters in road damage detection.

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

Citation Download Citation

Shenglang Liao "Road damage detection algorithm based on GhostNet lightweight YoLov8", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134160K (8 November 2024); https://doi.org/10.1117/12.3049529

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KEYWORDS

Roads

Detection and tracking algorithms

Damage detection

Feature extraction

Convolution

Performance modeling

Diseases and disorders

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