Abstract
Taxi has the characteristics of strong mobility and wide dispersion, which makes it difficult for relevant law enforcement officers to make accurate judgment on their illegal acts quickly and accurately. With the investment of intelligent transportation system, image analysis technology has become a new method to determine the illegal behavior of taxis, but the current image analysis method is still difficult to support the detection of illegal behavior of taxis in the actual complex image scene. To solve this problem, this study proposed a method of taxi violation recognition based on semantic segmentation of PSPNet and improved YOLOv3. (1) Based on YOLOv3, the proposed method introduces spatial pyramid pooling (SPP) for taxi recognition, which can convert vehicle feature images with different resolutions into feature vectors with the same dimension as the full connection layer and solve the problem of repeated extraction of YOLOv3 vehicle image features. (2) This method can recognize two different violations of taxi (blocking license plate and illegal parking) rather than only one. (3) Based on PSPNet semantic segmentation network, a taxi illegal parking detection method is proposed. This method can collect the global information of road condition images and aggregate the image information of different regions, so as to improve the ability to obtain the global information orderly and improve the accuracy of taxi illegal parking detection. The experimental results show that the proposed method has excellent recognition performance for the detection rate of license plate occlusion behavior DR is 85.3%, and the detection rate of taxi illegal parking phenomenon DR is 96.1%.
Highlights
In recent years, with the rapid development of economy and urbanization, the total amount of roads and vehicles in various cities in China has shown a trend of continuous growth [1]
In view of the low performance of current vehicle violation detection methods, this study proposed a new taxi violation detection method based on the improved YOLOv3 network and PSPNet network. e content is as follows: (1) In this study, the spatial pyramid pooling of semantic segmentation is merged into the traditional YOLOv3 network
Compared with rate determined by Zhang et al [23], the detection rate DR of the proposed method increases by 3.5%. is shows that the method in this study is better in the detection of license plate occlusion and can achieve the best effect in the scene of detecting smaller targets
Summary
With the rapid development of economy and urbanization, the total amount of roads and vehicles in various cities in China has shown a trend of continuous growth [1]. (2) is study proposed a detection method for illegal taxi parking based on PSPNet semantic segmentation network. It can comprehensively collect the global information of the road condition image to realize the orderly extraction of the image characteristics of the violation behavior. It further enhances the accuracy of taxi parking violation detection. E third section continues to introduce the method of identifying illegal taxi license plate occlusion based on the improved YOLOv3 network model. E fifth section realizes the simulation verification of the proposed taxi violation detection method based on the actual traffic collected images.
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