Weak Target Detection of Road Construction Vehicles via Multi-model Decision Fusion

Abstract

To address the challenges of construction vehicle detection caused by complex illumination and severe occlusion in transportation infrastructure scenarios, as well as the requirements for emergency rescue under sudden hazards, this paper proposes a detection system based on a multi-model decision fusion architecture. The system adopts a “coarse-screening and fine-inspection” strategy, utilizing YOLOv8 for rapid global screening to ensure real-time performance. For regions with low confidence, an improved DPFL-RetinaNet is introduced to perform secondary refined detection. Concurrently, this paper proposes the Decoupled Polynomial Focal Loss based on Taylor series expansion, which enhances the mining capability for weak targets through gradient reshaping. Experimental results demonstrate that the proposed system achieves an AP50 of 87.1% on the TCVD dataset, representing an improvement of nearly 30 percentage points over the standalone YOLOv8. This provides an efficient visual perception solution for construction safety and emergency rescue in complex environments, effectively balancing detection precision and inference speed. The proposed Confidence-Guided Cascade Decision System (CG-CDS) effectively addresses the inherent trade-off between computational efficiency and detection accuracy. By dynamically routing challenging samples to a specialized fine-grained detector, the system achieves robust performance across diverse environmental conditions including low illumination, severe occlusion, and complex background clutter. Extensive ablation studies confirm that each component of the cascaded architecture contributes positively to the overall detection performance, with the proposed Decoupled Polynomial Focal Loss (DPFL) providing the most significant improvement for weak target mining.