In the current development of intelligent traffic in many regions, vehicle target detection has always been the focus of attention and research in the field, which can further enhance the overall performance of multiple system modules such as the security module in intelligent traffic system (ITS). This article first analyzed traditional vehicle target detection techniques, which mainly used computer vision (CV) to recognize vehicle targets on the road. However, it was often susceptible to multiple factors such as the environment, and the efficiency and universality of vehicle target detection based on CV were also low. This article also conducted research on vehicle target detection technology based on wireless networks. In this process, both cloud computing and edge computing technologies were used to further improve the efficiency of vehicle target detection based on wireless network, and reduce the use requirements of vehicle target detection. This article selected 100 images to test the accuracy of two vehicle detection technologies that do not combine wireless network technology and wireless network technology. Among the 100 sample images, 50 included vehicles and 50 did not. Through experiments, it was found that the detection accuracy of cars and trucks without wireless network technology was 79% and 84%, respectively, while the detection accuracy of cars and trucks with wireless network technology was 92% and 89%, respectively. The wireless network-based vehicle detection technology improved the real-time performance of vehicle detection on the road with the help of cloud computing and edge computing technology, and enabled the vehicle detection technology to respond quickly according to system commands. The improvement of real-time performance and the implementation of rapid response have helped vehicle target detection technology achieve better performance in intelligent traffic.

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