Knowledge-Driven Multi-Scale Vehicle Detection Framework for Intelligent Transportation Systems

Abstract

In complex traffic environments, conventional vehicle detection methods often show limited precision and robustness when facing distant, small-scale, and occluded vehicles. To address these issues, this research proposes a Multiscale, Multi knowledge, Attention enhanced You Only Look Once (YOLO) model. It is a knowledge-driven multiscale vehicle detection framework for intelligent transportation systems, built on the YOLO version 8 nano model. The framework introduces three modules. The first is a cross-stage partial mixed aggregation network module. This module enhances backbone representation through dynamic multiscale aggregation. The second is a multiscale downsampling module that combines dilated convolution and parallel pooling to preserve vehicle cues during downsampling. The third module is an aspect-ratio perception with cross-attention module that injects aspect-ratio-aware knowledge with cross-attention to adapt to vehicle shape variation and suppress background interference. Experiments on the Vehicle dataset on Kaggle.com and the Berkeley DeepDrive 100K dataset showed that the Multiscale, Multi knowledge, Attention enhanced-YOLO model outperformed the lightweight YOLO baseline models. On the Berkeley DeepDrive 100,000 dataset, it achieved a 48.17% mean average precision @0.5 and 26.70% mean average precision @0.5:0.95, improving YOLO version 8 nano by 3.34% and 2.19%, respectively. Visualization results further confirmed its effectiveness under occlusion and adverse-weather conditions while maintaining real-time efficiency.