Research on Vehicle Detection Algorithm Based on Embedded ARM

Based on the theory of machine learning in the field of artificial intelligence, this paper proposes to use the computer vision platform OpenCV to construct an embedded ARM vehicle detection model. Determine the ARM embedded software and hardware and adopt Haar features for the Adaboost algorithm to design the OpenCV vehicle classifier. Cross-compile the ARM chip using Linux to generate new firmware for OpenMV. Use the DFU tool for embedded ARM chips to upgrade and re-burn them into the embedded development board for machine vision OpenMV. By using the classifier file and OpenCV’s image processing algorithm, the work of vehicle detection and recognition is completed, and the vehicle target is labeled with a candidate box in the picture and video. The results demonstrate that the algorithm in this paper maintains the leakage detection rate and false detection rate below 5% in four different working conditions: strong light, normal light, weak light, and nighttime, thereby fully demonstrating the effectiveness of the research conducted in this paper.