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Research on vehicle detection based on the regional feature fusion

Vehicle detection plays a crucial role in the decision-making, planning, and control of intelligent vehicles. It is one of the main tasks of environmental perception and an essential part of ensuring driving safety. In order to capture unique vehicle features and improve vehicle recognition efficiency, this paper fuses texture features of image and edge features of LIDAR to detect frontal vehicle targets. First, we use wavelet analysis and geometric analysis to segment the ground and determine the region of interest for vehicle detection. Then, the point cloud of the vehicle detected is projected into the image to locate the ROI. Moreover, the edge feature of the vehicle is guided to extract according to the maximum gradient direction of the vehicle’s rear contour. Furthermore, the Haar texture feature is integrated to identify the vehicle, and a filter is designed according to the point cloud’s spatial distribution to eliminate the error targets. Finally, it is verified by real-vehicle comparison tests that the proposed fusion method can effectively improve the vehicles’ detection with not much time.