Detecting Vehicles on the Edge: Knowledge Distillation to Improve Performance in Heterogeneous Road Traffic

The drastic growth in the number of vehicles in the last few decades has necessitated significantly better traffic management and planning. To manage the traffic efficiently, traffic volume is an essential parameter. Most methods solve the vehicle counting problem under the assumption of state-of-the-art computation power. With the recent growth in cost-effective Internet of Things (IoT) devices and edge computing, several machine learning models are being tailored for such devices. Solving the traffic count problem on these devices will enable us to create a real-time dashboard of network-wide live traffic analytics. This paper proposes a Detect-Track-Count (DTC) framework to count vehicles efficiently on edge devices. The proposed solution aims at improving the performance of tiny vehicle detection models using an ensemble knowledge distillation technique. Experimental results on multiple datasets show that the custom knowledge distillation setup helps generalize a tiny object detector better.