Intelligent Vehicle Collision-Avoidance System with Deep Learning
Yeong-Kang Lai, Chu-Ying Ho, Yu-Hau Huang, Chuan-Wei Huang, Yi-Xian Kuo, Yu-Chieh Chung · 2018
In this paper, we demonstrate and evaluate a method to perform real-time object detection with unmanned vehicle using the state of the art, MobileNets, with object detection algorithm running on an NVIDIA Jetson TX2, an GPU platform targeted at power constrained mobile applications that use neural networks under the hood. This, as a result of comparing several cutting edge object detection algorithms. Multiple evaluations we present provide insights that help choose the optimal object detection configuration given certain frame rate and detection accuracy requirements. We propose how this setup running on-board a unmanned vehicle can be used to process a video feedback during emergencies in real-time, and feed a decision support warning system using the generated detections.