Donzella, Valentina, Chan, Pak Hung and Huggett, Anthony (2022) Optimising Faster R-CNN training to enable video camera compression for assisted and automated driving systems. In: 2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI 2022), Singapore, 9-11 Dec 2022 (In Press)

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Abstract

Advanced driving assistance systems based on only one camera or one RADAR are evolving into the current assisted and automated driving functions delivering SAE Level 2 and above capabilities. A suite of environmental perception sensors is required to achieve safe and reliable planning and navigation in future vehicles equipped with these capabilities. The sensor suite, based on several cameras, LiDARs, RADARs and ultrasonic sensors, needs to be adequate to provide sufficient (and redundant, depending on the level of driving automation) spatial and temporal coverage of the environment around the vehicle. However, the data amount produced by the sensor suite can easily exceed a few tens of Gb/s, with a single ‘average’ automotive camera producing more than 3 Gb/s. It is therefore important to consider leveraging traditional video compression techniques as well as to investigate novel ones to reduce the amount of video camera data to be transmitted to the vehicle processing unit(s). In this paper, we demonstrate that lossy compression schemes, with high compression ratios (up to 1:1,000) can be applied safely to the camera video data stream when machine learning based object detection is used to consume the sensor data. We show that transfer learning can be used to re-train a deep neural network with H.264 and H.265 compliant compressed data, and it allows the network performance to be optimised based on the compression level of the generated sensor data. Moreover, this form of transfer learning improves the neural network performance when evaluating uncompressed data, increasing its robustness to real world variations of the data.

Item Type:	Conference Item (Paper)
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions:	Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Automobile driving, Automobiles -- Design and construction, Automated vehicles -- Technological innovations, Automated guided vehicle systems, Radar, Wireless sensor networks, Robot vision
Publisher:	IEEE
Official Date:	2022
Dates:	Date Event 2022 Available 24 August 2022 Accepted
Status:	Peer Reviewed
Publication Status:	In Press
Reuse Statement (publisher, data, author rights):	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	13 October 2022
Date of first compliant Open Access:	14 October 2022
Conference Paper Type:	Paper
Title of Event:	2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI 2022)
Type of Event:	Conference
Location of Event:	Singapore
Date(s) of Event:	9-11 Dec 2022
Related URLs:	Publisher

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