| Summary: | Expressway traffic event detection at night is essential for improving rescue efficiency and avoiding secondary accidents. Most expressways in China have built a complete Expressway video monitoring system. However, at night, the expressway traffic event detection still adopts manual detection, which is inefficient. In this dissertation, the strategy of expressway traffic event detection at night has been analysed first. On this basis, by combining the Mask method and SpyNet deep learning, this study develops a night highway vehicle detection deep learning network with a dense optical flow formed by night vehicle light flow as the detection object. Finally, the Deepsort algorithm is used to track and measure the velocity of the detected target. The measured data are used to compare the background difference method, classical optical flow method, YOLO-v3 and proposed method in this paper. The results show that the proposed method has the advantages of high detection accuracy and fast detection speed.
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