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Vehicle detection in diverse traffic using an ensemble convolutional neural backbone via feature concatenation

Deshmukh, P and Majhi, S and Sahoo, UK and Das, SK (2023) Vehicle detection in diverse traffic using an ensemble convolutional neural backbone via feature concatenation. In: Transportation Letters .

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Official URL: https://doi.org/10.1080/19427867.2023.2250622


Nowadays, deploying an intelligent vehicle detection system (IVDS) in diverse traffic is a work priority. It provides real-time traffic information with vehicle counts and types of vehicles. IVDS deployment in diverse traffic is challenging because different vehicle classes occlude each other on the road. In recent years, convolutional neural network (CNN) based deep learning (DL) methods have attained incredible progress in implementing IVDS. However, most CNN-based DL methods do not include diverse traffic conditions in Asian countries. Also, due to existing feature extraction backbones, they cannot accurately detect multi-scale vehicles. This work proposes an advanced visual computing deep learning (AVCDL) method with a vast labeled vehicle dataset to detect vehicles in diverse traffic. It includes an ensemble backbone and an improved multi-stage vehicle detection head (MSVDH). An ensemble CNN backbone extracts the vehicle features and combines them on a single channel via a feature concatenation. The final detection is carried out by an improved MSVDH that classifies the target vehicles. The proposed method is examined, tested, and evaluated using traffic statistics. It is contrasted with current cutting-edge vehicle detection techniques. It achieves 86.32 mean average precision (mAP) on self-collected diverse traffic labeled dataset (DTLD) and 86.17 mAP on KITTI. Moreover, the real-time performance is validated with NVIDIA Jetson Tx2 and Nano boards. It achieves 15 frames per second (FPS) on Jetson Tx2 and 7 FPS on Jetson Nano. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Item Type: Journal Article
Publication: Transportation Letters
Publisher: Taylor and Francis Ltd.
Additional Information: The copyright for this article belongs to the Taylor and Francis Ltd.
Keywords: Convolution; Convolutional neural networks; Deep learning; Feature extraction; Learning systems; Traffic surveys, Convolutional neural network; Deep learning; Diverse traffic; Feature concatenation; Frames per seconds; Learning methods; Multi-stages; Network-based; Vehicle detection systems; Vehicles detection, Vehicles
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 07 Nov 2023 05:49
Last Modified: 07 Nov 2023 05:49
URI: https://eprints.iisc.ac.in/id/eprint/83075

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