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7 August 2024 Research on target detection algorithm for foggy weather based on YOLO-FOG
Fang Gu, Kai Zhu
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Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 1322936 (2024) https://doi.org/10.1117/12.3038187
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China
Abstract
Images acquired under foggy conditions have blurred lighting and low resolution and contrast, while the feature extraction of traditional target detection designs does not take into account the fog obscuring the target, affecting the recognition rate of targets such as vehicles and pedestrians. Based on this, this paper proposes YOLO-FOG. Based on Anchor-Free, the TSCODE module is inserted to further separate the feature encoding of the two tasks; MobileViT is introduced into the pyramid structure of the feature graph to construct the long-distance dependencies of the features and integrate the features. When evaluated using the RTTS dataset, the mAP values of Bicycle, Bus, Car, Motorbike, and Person with their five types of targets can reach 82.19%, 81.36%, 89.47%, 75.89%, and 84.01%, respectively, and meanwhile the simultaneous detection speed up to 65.52 fps, which proves that this paper's method combines both accuracy and speed, and has good application prospects.
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Fang Gu and Kai Zhu "Research on target detection algorithm for foggy weather based on YOLO-FOG", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 1322936 (7 August 2024); https://doi.org/10.1117/12.3038187
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KEYWORDS
Target detection
Detection and tracking algorithms
Fiber optic gyroscopes
Data modeling
Feature extraction
Classification systems
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