September 10, 2025ITM Web of ConferencesOpen Access

Research on The Vehicle Detection Technology Based on The Yolo Model

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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.

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Research evaluates YOLO's impact on vehicle detection accuracy and speed, suggesting improvements needed for small targets.

Lihang Cao (2025) studied this question.

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