Roadside Perception Applications Based on DCAM Fusion and Lightweight Millimeter-Wave Radar–Vision Integration

With the advancement in intelligent transportation systems, single-sensor perception solutions face inherent limitations. To address the constraints of monocular vision detection, this study presents a vehicle road detection system that integrates millimeter-wave radar and visual information. By generating mask maps from millimeter-wave radar point clouds, radar data transition from a global assistance role to localized guidance, identifying vehicle target positions within RGB images. These mask maps, along with RGB images, are processed by a Dual Cross-Attention Module (DCAM), where the fused features are fed into an enhanced YOLOv5 network, improving target localization accuracy. The proposed dual-input DCAM enables dynamic feature fusion, allowing the model to adjust its reliance on visual and radar data according to environmental conditions. To optimize the network architecture, ShuffleNetv2 replaces the YOLOv5 Backbone, while the Ghost Module is incorporated into the Neck, creating a lightweight design. Pruning techniques are applied to reduce model complexity, making it suitable for embedded applications and real-time detection scenarios. The experimental results demonstrate that this fusion scheme effectively improves vehicle detection accuracy and robustness compared to YOLOv5, with accuracy increasing from 59.4% to 67.2%. The number of parameters is reduced from 7.05 M to 2.52 M, providing a precise and reliable solution for intelligent transportation and roadside perception.