Due to more bad weather conditions such as fog, rain and snow in the road driving environment, resulting in low target recognition accuracy and missed detection problems in the traffic sign detection algorithm in autonomous driving technology, this paper proposed an improved YOLOv7-based traffic sign detection algorithm. Firstly, the MP conv structure of YOLOv7 was improved and the AC mix attention mechanism was embedded to reduce the feature loss caused during the network feature processing and improve the feature expression capability of the model. Secondly combined with the traffic sign image features, the Kmeans++ algorithm was introduced to re-cluster the anchor frame size and the soft-NMS non-maximum suppression algorithm was introduced. Finally, an image data enhancement algorithm was written in Python to simulate the generation of traffic sign images for foggy scenes to enhance the dataset. Experiments were conducted on the Chinese traffic sign dataset CCTSDB, and the results showed that the improved YOLOv7 algorithm achieved 93.8% recognition accuracy, 91.5% recall, 95.7% average accuracy and 99 frames in complex scenes such as fog, and all indicators were improved compared with the mainstream detection algorithms, which can achieve traffic sign detection in bad weather such as fog.


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    Title :

    Traffic sign detection algorithm for foggy weather scenarios


    Contributors:

    Conference:

    Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023) ; 2023 ; Kuala Lumpur, Malaysia


    Published in:

    Proc. SPIE ; 12799


    Publication date :

    2023-10-10





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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