In the process of truck overload and over-limit detection, it is necessary to detect the characteristics of the vehicle's size, type, and wheel number. In addition, in some vehicle vision-based load recognition systems, the vehicle load can be calculated by detecting the vibration frequency of specific parts of the vehicle or the change in the length of the suspension during the vehicle's forward process. Therefore, it is essential to quickly and accurately identify vehicle features through the camera. This paper proposes a vehicle feature recognition method based on image semantic segmentation and Python, which can identify the length, height, number of wheels and vibration frequency at specific parts of the vehicle based on the vehicle driving video captured by the roadside camera. The process of vehicle recognition is as follows: First, build a convolutional neural network based on the image semantic segmentation model SegNet and use the data set to train it, and use the Python program to perform subsequent operations such as vehicle size measurement and vibration frequency recording. Then, the vehicle body and wheels are marked in a single video frame. According to the distance between the vehicle and the camera and the size of the vehicle in the picture, the various sizes of the vehicle can be calculated. According to the height change of the edge just above the axle in different frames of the video, the vibration frequency change of the axle suspension can be recorded, and the function image can be drawn. This technology can be used in the vision-based overload detection system to improve the efficiency of truck overload detection and promote the development of intelligent transportation.