Real-time and accurate object detection on edge device with TensorFlow Lite

摘要

Abstract Objection detection is of vital importance to many fields, such as autonomous driving, outdoor robotics, and computer vision. Existing approaches on object detection can hardly run on the resource-constrained edge devices. In order to mitigate this dilemma, we propose to apply TensorFlow Lite to convert Float32 neural network model to unit8 neural network with subtle or even no accuracy loss. Two advantages are here for conversion. First, it reduces the model size to a quarter so that it fits for devices with limited storage. Second, it achieves much faster inference time. I conduct an experiment on MSCOCO dataset. Experimental results show that our proposed method achieves mAP 72.1 and FPS 23 on edge device.