AI RESEARCH
Edge AI for Automotive Vulnerable Road User Safety: Deployable Detection via Knowledge Distillation
arXiv CS.LG
•
ArXi:2604.26857v1 Announce Type: cross Deploying accurate object detection for Vulnerable Road User (VRU) safety on edge hardware requires balancing model capacity against computational constraints. Large models achieve high accuracy but fail under INT8 quantization required for edge deployment, while small models sacrifice detection performance. This paper presents a knowledge distillation (KD) framework that trains a compact YOLOv8-S student (11.2M parameters) to mimic a YOLOv8-L teacher (43.7M parameters), achieving 3.9x compression while preserving quantization robustness.