For 2D image data target detection, this paper proposes an improved detection algorithm for YoloV5s based on the attention mechanism. By replacing the Focus module in YoloV5s with a CBL module, it facilitates model export. The four attention modules, SELayer, EcaLayer, CBAM and CoordAtt are added to the convolutional network of YoLoV5s. Before the P4 layer, before the P5 layer, before the SPPF layer and before the last layer of the original backbone network, respectively. By experimenting with algorithms that include YoloV5s, It was concluded that among the five algorithms tested for evaluation. The proposed YoloV5s-CoordAtt has the best performance level in terms of accuracy, with a 4.48% improvement compared to the original algorithm.
With the impact of COVID-19, more people are choosing to travel by private cars, which will cause problems such as traffic congestion. It is essential for traffic engineers to have real-time traffic volume, speed, and individual vehicle length. In this study, the ACC7350 millimeter-wave radar was tested, and its advantages and disadvantages were analyzed in vehicle speed, distance from the radar, and vehicle trajectory. The speed detection error between MWR and GPS was within ±6%, and the distance detection error was ±20%. Then the traffic flow detection results of the camera and millimeter-wave radar were compared and analyzed. Results show that the mistakes of traffic flow detection based on vision and MWR are ±4% and ±13%, respectively. Finally, we proposed a traffic data processing method combined with a camera-based target tracking algorithm.
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