Detection of Dangerous Motorcycling Using YOLO and Machine Learning Classifiers
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Abstract
The work studied three methods to identify risky motorcycle riding. It aids in identifying risky motorcyclists who adopt unusual riding positions, which lead to a rise in traffic accidents. This work established the feasibility of monitoring hazardous riding on public roadways. We investigated the detection of motorcycle riding types using 1) the motorcycle's extracted images, 2) the motorcyclist's extracted images, and 3) the motorcyclist's pose landmarks. You Only Look Once (YOLO) was applied to detect a motorcycle, a motorcyclist, and the landmarks of a motorcyclist from images. The findings indicated that the classification derived from YOLO detectable motorcycles surpassed that of the motorcyclists and their pose landmarks. The VGG16 surpassed MobileNet, CNN, and ResNet50 in classifying normal and dangerous riding. YOLO's efficacy in identifying specific pose landmarks at night was insouciant. Detecting dangerous motorcycling based on the motorcyclists' pose landmarks was ineffective at night. Identifying dangerous motorcycling from the detected motorcycles was the most effective. The findings indicated that YOLO attained an accuracy of 71.09% in motorcycle detection from daytime and nighttime images, whereas VGG16 acquired an accuracy of 98.75% in recognizing dangerous motorcycling.
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