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Performance comparison of different CNN models for indian road dataset

Mukhopadhyay, A and Biswas, P and Agarwal, A and Mukherjee, I (2019) Performance comparison of different CNN models for indian road dataset. In: 3rd International Conference on Graphics and Signal Processing, ICGSP 2019, 1 June 2019-3 June 2019, Hong Kong, pp. 29-33.

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Official URL: https://doi.org/10.1145/3338472.3338480


Recent advancement in the field of computer vision and development of Deep Neural Network based object detection led researchers and industries to focus on autonomous vehicles. This paper aims to find how accurately previously proposed CNN architectures detect on-road obstacles in Indian road scenarios in the context of autonomous vehicle. We have compared three different convolution neural networks trained with COCO dataset for detecting autorickshaws in Indian road. We undertook statistical hypothesis testing to find effect of these three models, i.e. YOLOv3, Mask R-CNN, and RetinaNet on detection accuracy rate. While measuring accuracy, we have noted that detection accuracy rate of RetinaNet is significantly better than other two CNN architectures. Although there is no significant difference between other two networks in context of detection rate. The accuracy rate shows the performance of RetinaNet invariant to autorickshaws' color and shape, and different climatic and complex background scenarios. © 2019 Association for Computing Machinery. All rights reserved.

Item Type: Conference Paper
Publication: ACM International Conference Proceeding Series
Publisher: Association for Computing Machinery
Additional Information: The copyright for this article belongs to Association for Computing Machinery.
Keywords: Autonomous vehicles; Computer vision; Convolution; Deep neural networks; Network architecture; Object detection; Object recognition; Roads and streets; Testing, Accuracy rate; Complex background; Convolution neural network; Detection accuracy; Detection rates; Measuring accuracy; Performance comparison; Statistical hypothesis testing, Neural networks
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 24 Dec 2022 09:34
Last Modified: 24 Dec 2022 09:34
URI: https://eprints.iisc.ac.in/id/eprint/78542

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