Real‐time CVSA decals recognition system using deep convolutional neural network architectures

IET Intelligent Transport Systems

Juan Yépez¹  Younhee Choi¹  Riel Castro‐Zunti¹  Seok‐Bum Ko¹ 

[1] Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada;

关键词: Image recognition;    Computer vision and image processing techniques;    Traffic engineering computing;    Neural nets;   

DOI  :  10.1049/itr2.12103

来源: DOAJ

【 摘 要 】

Abstract The Commercial Vehicle Safety Alliance (CVSA) aims to achieve uniformity, compatibility and reciprocity of commercial motor vehicle inspections and enforcement by certified inspectors dedicated to driver and vehicle safety. Commercial vehicles that pass a CVSA inspection are eligible for a decal representing a commitment to safety. In this work, we propose a two‐step automatic CVSA decal recognition system using deep convolutional neural network architectures. The first step localizes a vehicle's windshield and the CVSA decal within, and classifies the decal colour. The CVSA decal is cropped and used as input to the second stage, which localizes and classifies a digit and the corner‐cut of a CVSA decal. With the corner cut, colour, and digit, the system can determine the decal's date of issue. We use as our baseline the MobileDet architecture, customizing the backbone to our tasks. Our first custom architecture is larger than the baseline because it needs more representational power to detect small decals within an image. The second architecture is much smaller because digit and corner‐cut recognition is a simpler task. Our custom architectures reduce processing time and exceed accuracies relative to pre‐trained architectures. We implemented our models on different edge hardware accelerators (e.g. the Google Coral, Nvidia Jetsons, and Intel NCS) and compared the performance when processing a real‐time video stream. Our system can predict frames at 173.31 FPS on an NVIDIA Jetson AGX Xavier with 98.5% mean average precision @ 0.5 IoU.

【 授权许可】

Unknown