Real-Time Object Classification in Autonomous Driving: A Lightweight Residual CNN Approach on Unified BDD100K–COCO Data

Unified Multi-Dataset Super-Taxonomy: A novel three-class (Vehicle, Pedestrian, Obstacle) crop-based data unification pipeline integrating BDD100K and MS COCO 2017, yielding 58,831 annotated ROI samples with a consistent label space for real-time classification.
Lightweight Residual CNN Architecture: A custom four-stage residual backbone (channels 32→512) with global average pooling and dropout regularisation, specifically designed for resource-constrained on-board automotive hardware.
Speed–Accuracy Optimisation: Achievement of 215.5 FPS inference throughput via 224×224 input resolution and FP16 mixed-precision acceleration while maintaining 93% mean classification accuracy and per-class F1-scores of 0.94/0.94/0.91.