Comparing cnns for non-conventional traffic participants

Mukhopadhyay, A and Mukherjee, I and Biswas, P (2019) Comparing cnns for non-conventional traffic participants. In: 11th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2019, 21 September 2019, Utrecht, pp. 171-175.

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Abstract

This paper investigates performance of three state-of-the-art pretrained Convolutional Neural Network (CNN) models in terms of accuracy and latency for on and off-road obstacle detection in context of autonomous vehicle in Indian road. We investigated performance of Mask R-CNN, RetinaNet, and YOLOv3 on publicly available Indian road dataset. We evaluated accuracy and latency of these models on novel classes of objects such as animals, autorickshaws, caravan. Our results show that accuracy of Mask R-CNN is significantly higher than YOLOv3 and RetinaNet. We have also found Yolov3 is significantly higher than RetinaNet. We have also tested latency of the CNN models and found that latency of YOLOv3 is significantly lower than other two models and RetinaNet is significantly faster than Mask R-CNN. Finally, we have proposed an expert system to integrate environment parameters inside car along with outside car obstacles detected by YOLOv3 to estimate cognitive load of co-passengers of autonomous vehicle. © 2019 Copyright is held by the owner/author(s).

Item Type:	Conference Paper
Publication:	Adjunct Proceedings - 11th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2019
Publisher:	Association for Computing Machinery, Inc
Additional Information:	The copyright for this article belongs to Association for Computing Machinery, Inc
Keywords:	Autonomous vehicles; Convolution; Expert systems; Neural networks; Object detection; Obstacle detectors; Off road vehicles; Roads and streets, CNN models; Cognitive loads; Convolutional neural network; In contexts; Obstacle detection; State of the art, User interfaces
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation Division of Mechanical Sciences > Centre for Product Design & Manufacturing Others
Date Deposited:	05 Jan 2023 11:50
Last Modified:	05 Jan 2023 11:50
URI:	https://eprints.iisc.ac.in/id/eprint/78797

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