Drone-Supervised Multimodal Sensor Fusion for Infrastructure-Based Vehicle Detection in Bird’s Eye View

Smart intersection monitoring systems face critical limitations when relying on single-sensor modalities, with cameras degrading under poor visibility and radar struggling with sparse point clouds. This work presents a novel multi-modal sensor fusion framework that integrates radar and camera detections through adaptive range-dependent strategies for robust vehicle detection across diverse operational conditions. We develop a radar detection pipeline that leverages drone-supervised learning to transform sparse radar points into precise 2D bounding boxes, achieving 93.0% precision through feature consistency filtering and motion-aware association. A geometry-aware camera-to-bird’s-eye-view transformation architecture combines homography priors with residual neural networks to enable direct spatial comparison between modalities, achieving 0.494 average IoU with lane-aware geometric correction. The fusion framework implements four distinct combination strategies—weighted IoU, range-adaptive, envelope, and intersection fusion—evaluated against 19,612 ground truth bounding boxes across six range bins. Results demonstrate universal fusion superiority with IoU improvements ranging from 6.1% to 28.3% across all operational ranges, achieving 30.4% overall ground truth win rate and 22.6% average IoU enhancement. Range-based analysis reveals distinct operational zones: camera dominance at close range (0-40m), fusion optimization in transition zones (40-80m), and progressive radar emergence through fusion support (80-100m), leading to radar effectiveness beyond 100m. The range-adaptive fusion strategy achieves peak IoU of 0.708 at 60–80m, highlighting intelligent sensor integration as key for intersection safety and guiding deployment strategies for next-generation transportation systems.