RDLNet: a channel pruning-based traffic object detection algorithm

Abstract Accurate traffic object detection plays a critical role in intelligent transportation systems, yet it faces challenges in balancing real-time performance with detection accuracy. This challenge persists particularly under complex scenarios involving occlusions, multi-scale objects, and dynamic environmental changes. To address these challenges, this paper introduces RDLNet (RepViT backbone with a DBB-Gelan-based Neck (DG Neck) and Layer-Adaptive Magnitude-based Pruning (LAMP) Network), a lightweight traffic detection framework that synergistically integrates adaptive channel pruning and multi-scale feature refinement. The proposed architecture achieves enhanced model efficiency while preserving detection accuracy through hierarchical feature fusion mechanisms. The conventional backbone is replaced by RepViT, which enables the capture of multi-scale spatial dependencies. A dynamic feature fusion neck network called DG-Neck, composed of stacked DG modules, is introduced to adaptively aggregate features from different layers and enhance multi-scale feature discrimination. Additionally, Layer-Adaptive Magnitude-based Pruning is applied to compress the model by selectively removing redundant channels while preserving essential detection pathways. Comprehensive experimental evaluations confirm RDLNet’s superiority over state-of-the-art methods. On the DAWN dataset, RDLNet demonstrates significant advantages over YOLOv8n by achieving 39.5% fewer parameters and reducing FLOPs by 14.8%. It also delivers substantial detection improvements, with 43.5% higher precision, 23.1% increased recall, and gains of 14.0% and 9.0% in mAP 50 and mAP 50-95, ultimately reaching 0.366 and 0.205. Comparative experiments with advanced models consistently reveal lower parameter counts and enhanced detection performance. Furthermore, its robustness has been validated on the VOC2012 dataset. RDLNet’s lightweight design and hierarchical feature learning provide a deployable solution for resource-constrained edge environments, thereby advancing real-time traffic monitoring systems through optimized accuracy-efficiency trade-offs.