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September 10, 2025Open Access

Research on The Vehicle Detection Technology Based on The Yolo Model

Key Points

The YOLO-based vehicle detection algorithm achieves a mean Average Precision (mAP) of 70.3%, ensuring effective real-time processing.
Testing revealed a detection speed of 80.7 FPS, demonstrating the algorithm's suitability for intelligent transportation systems.
Despite high accuracy, the method struggles with detecting small and densely packed vehicle targets, indicating room for improvement.
Utilizing the PASCAL VOC dataset, the model was trained using a convolutional neural network and gradient descent for optimal performance.

Abstract

Vehicle detection is the most basic and important part of intelligent transportation systems and vehicle driving technology. Vehicle detection is mainly responsible for finding all vehicles in a given image and giving their bounding boxes. From the point of view of safety and practicality, vehicle detectors need to have very high detection accuracy and be able to complete real-time processing. Based on PASCAL VOC data set, this paper explores the application of YOLO target detection algorithm in the vehicle detection field. The convolutional neural network is trained by gradient descent method and tested with the same test set. On the test set, the vehicle detection algorithm mAP based on YOLO implementation is 70.3%, and the detection speed is 80.7FPS. According to the research results, the vehicle detection algorithm based on YOLO has reached the standard of real-time processing, but the detection effect of small and dense car targets is poor.

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