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In ITS, image processing technology is applied to a wide variety of areas such as visual-based intelligent vehicle navigation, visual-based traffic monitoring and visual-based traffic management. In the recognition system of the vehicle body characteristics, most of the recognition is the license plate and the car emblem, etc. This paper proposes an image recognition application method for the vertical motion of the car while driving, mainly including vertical height detection and vertical displacement velocity acceleration recognition. The edge detection model of the image object is established by using the gray image to obtain the car motion segmentation image. At the same time, an image length and actual length coordinate conversion model is established, which can calculate an arbitrary actual length of the image object. In this paper, Yuejin Shangjun X500 van was selected as the test vehicle, and the video data was captured with a camera.

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.

Dangerous driving behavior will cause serious traffic accidents, which will not only threaten life and property but also cause traffic congestion and reduce road capacity. Speed detection is an important detection method to identify whether a driver is driving dangerously. Traditional speed detection methods need additional sensors, which will increase the cost of speed measurement. This paper proposes a vehicle speed estimation algorithm based on the imaginary projection plane (IPP). The IPP will be established according to the height, field angle, and vertical tilt angle of the camera and will be used to establish the mapping relationship between the world coordinates and image coordinates of the vehicle. By combining YOLOv4 and DeepSORT, the vehicle license plate is detected and tracked, and the center point of the vehicle license plate is taken as the feature point of vehicle speed estimation. The vehicle speed is estimated according to the IPP.

Intelligent sweeper vehicle is gradually applied to human life, in which the accuracy of garbage identification and classification can improve cleaning efficiency and save labor cost. Although Deep Learning has made significant progress in computer vision and the application of semantic network segmentation can improve waste identification rate and classification accuracy. Due to the loss of some spatial information during the convolution process, coupled with the lack of specific datasets for garbage identification, the training of the network and the improvement of recognition and classification accuracy are affected. Based on the Unet algorithm, in this paper we adjust the number of input and output channels in the convolutional layer to improve the speed during the feature extraction part. In addition, manually generated datasets are used to greatly improve the robustness of the model.

Signage recognition is one of the hot topics in recent years. It has important applications in intelligent traffic and autonomous driving of smart cars. This paper designs a road marking recognition method combining OPSA point set distance and wavelet transform. The method consists of three main phases: 1) image denoising, restoration, 2) feature extraction, and 3) image recognition. First, a Gaussian-smoothing filter used to attenuate or remove irrelevant information in the image, enhance related information in the image, and achieve image denoising. In the feature extraction stage, the feature extraction and recognition method based on wavelet transform adopted to overcome the deficiency of the traditional Fourier feature extraction method, ensure that high frequency information is not lost, and low frequency information is not lost. Finally, the OSPA point set used to identify distance markers.

As motor assembly of Battery Electric Vehicle (BEV) replaces engine system of Internal Combustion Engine (ICE) vehicle, interior structure-borne noise induced by road random excitation becomes more prominent under middle and high speed. The research is focused on central driving type BEV. In order to improve interior noise in middle and low frequency range, dynamic load of BEV body must be identified. Consequently the structural noise induced by road excitation is conducted. The limitations of common identification method for dynamic body load are analyzed. The applied several identification methods are proposed for deterministic dynamic load such as engine or motor. Random dynamic load generated by road excitation is different from deterministic dynamic load. The deterministic load identification method cannot be applied to the random load directly. An identification method of dynamic body load for BEV is presented based on power spectrum decomposition.

The acquisition of vehicle dimensions in a vehicle’s moving process has a wide application in road monitoring, transportation, vehicle model recognition and non-contact overload recognition. At present, the detection of the vehicle dimensions mostly adopts the methods of human visual inspection and tool detection, which has a low detection efficiency and difficult to replicate on a large scale. Based on the image background subtraction method, this paper proposes a vehicle dimensions detection method, which can realize real-time detection of road vehicle dimensions. This method uses an adaptive Gaussian Mixture Model (GMM) to establish a background model based on the video stream. Initially, the moving target image is obtained by the background subtraction method, and then the edge detection under the Canny operator and Hough transform circle detection are performed on the image to obtain the pixel dimension of the vehicle's outline.

Before the new car rolls from the line, due to assembly errors, inaccurate four-wheel positioning, etc., the vehicle will run off on a flat road, which will affect the driving comfort and safety. At present, most automobile manufacturers choose to perform the deviation test in the process of vehicle rolls from the line. Compared with other detection methods, the online deviation test system is developed with high precision of laser ranging, fast response and good reliability, which can realize fast and high-precision detection of vehicle deviation. In this paper, test system software is developed based on the LABVIEW, a variety of communication methods to build the communication system, using information check and queue task processing to control, to meet the test needs. Firstly, the calculation model of the deviation of the test system is established.

An accurate and timely positioning of the vehicle is required at all times for autonomous driving. The global navigation satellite system (GNSS), even when integrated with costly inertial measurement units (IMUs), would often fail to provide high-accuracy positioning due to GNSS-challenged environments such as urban canyons. As a result, visual odometry is proposed as an effective complimentary approach. Although it’s widely recognized that visual odometry should be developed based on both visible and infrared images to address issues such as frequent changes in ambient lightening conditions, the mechanism of visible-infrared fusion is often poorly designed. This study proposes a Generative Adversarial Network (GAN) based model comprises a generator, which aims to produce a fused image combining infrared intensities and visible gradients, and a discriminator whose target is to force the fused image to retain as many details that exist mostly in visible images as possible.