An Embedded Deep Learning Object Detection Model For Traffic In Asian Countries

Abstract

Deep learning-based object detection algorithms have been developed through extensive research and achieved impressive performance. However, due to the heavy computation of deep convolutional neural networks, it is not suitable to deploy all object detection algorithms on resource-constrained embedded systems, such as NVIDIA Jetson TX2. In this paper, a size-efficient YOLO v3-based model is adopted with two key points, a better backbone convolutional neural network and a fusing structure for the multiscale feature map. We have deployed the model on NVIDIA Jetson TX2 to test the performance of accuracy, model size, computational complexity and detecting time. Furthermore, this methodology has been awarded as the best bicycle detector in the 2020 embedded deep learning object detection model compression competition for traffic in Asian countries.

Tasks

Reproductions