An improved YOLOv8 detection algorithm for traffic signs

Zihan Gui; Xiaofeng Lian; Maomao Kang

doi:10.1117/12.3031073

19 July 2024 An improved YOLOv8 detection algorithm for traffic signs

Zihan Gui, Xiaofeng Lian, Maomao Kang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131812V (2024) https://doi.org/10.1117/12.3031073
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

To address the current issues of slow detection speed and poor accuracy for small targets in traffic sign detection, an optimized model based on YOLOv8 is proposed. This model integrates a small target detection layer, merging deep and shallow semantics to reduce semantic loss due to scale inconsistency. Additionally, it employs the lightweight EfficientNetV2 as the backbone network to decrease model parameters and enhance performance. The Coordinate Attention Mechanism is also incorporated, improving feature localization. Testing on the TT100K dataset, this model surpasses YOLOv8, with an accuracy increase of 7.1%, mAP@0.5 increased by 5.8%, a weight file of 5.92MB, and a detection rate of 279FPS. The model shows improved accuracy and performance while maintaining a simpler structure.

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

Citation Download Citation

Zihan Gui, Xiaofeng Lian, and Maomao Kang "An improved YOLOv8 detection algorithm for traffic signs", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131812V (19 July 2024); https://doi.org/10.1117/12.3031073

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KEYWORDS

Target detection

Small targets

Detection and tracking algorithms

Convolution

Head

Education and training

Mathematical optimization

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