Vehicle Detection from UAV Perspective Based on GBC-YOLOv5s in the Low-Altitude Economy

This paper presents GBC-YOLOv5s, a lightweight vehicle detection model designed for unmanned aerial vehicle (UAV) applications in the growing low-altitude economy sector. By utilizing the baseline YOLOv5s architecture, we integrated three structural enhancements to achieve an optimal trade-off between algorithmic precision and processing expenses. First, ghost convolution is used to exploit feature map redundancy, substantially cutting both parameter count and computational overhead. Second, a Bi-Level routing attention (BRA) mechanism is integrated. This allows the model to capture global context along with fine-grained local features of vehicle targets effectively. Third, standard upsampling is replaced with content-aware reassembly of features (CARAFE). This better preserves feature details, which proves particularly important for detecting small objects. Testing was conducted on a custom UAV-based vehicle dataset. GBC-YOLOv5s achieves 86.6% average precision (AP) with only 4.7 M parameters. This represents a 34.7% reduction from the original YOLOv5s with 7.2 M parameters. The accuracy drop is merely 0.2%. Ablation experiments validate both the individual impact and synergistic nature of each proposed module. Results show that GBC-YOLOv5s reaches a practical balance between performance and efficiency. It offers a workable solution for real-time vehicle detection on resource-limited UAV platforms and intelligent traffic monitoring applications.