Faster RCNN based Vehicle Detection and Counting Framework for Undisciplined Traffic Conditions

Ahmed, Syeda Hafsa; Raza, Mehwish; Mehdi, Syeda Shajeeha; Rehman, Inshal; Kazmi, Majida; Qazi, Saad Ahmed

doi:10.1109/honet53078.2021.9615466

Cited by 4 publications

(1 citation statement)

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“…The implementation of the proposed framework is done using a pre-trained faster R-CNN TensorFlow model as given in [2]. Transfer learning involves using already built network architecture and models that have been developed for similar tasks and refining and returning it for the required purpose.…”

Section: Transfer Learningmentioning

confidence: 99%

Towards the next generation intelligent transportation system: A vehicle detection and counting framework for undisciplined traffic conditions

Ahmed¹,

Raza²,

Kazmi³

et al. 2023

NNW

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Modern development in deep learning and computer vision techniques, intelligent transportation system (ITS) has emerged as a useful tool for building a traffic infrastructure in smart cities. Previously, several computer vision techniques have been proposed for vehicle recognition, which were limited in handling undisciplined, dense and laneless traffic conditions. Moreover, these frameworks did not incorporate many of the local vehicle configurations common in South Asian countries such as Pakistan, Bangladesh, and India. Considering the limitations of previous frameworks, this paper presents efficient vehicle detection and counting model for undisciplined conditions including dense and laneless traffic, occulusion cases and diverse range of local vehicles. A dataset of more than 2400 images of vehicles has been collected comprising of six new categories of local vehicles, and considering undisciplined traffic conditions to ensure robustness in vehicle detection and counting system. Transfer learning based technique has been used, using faster R-CNN model with Inception V2 as underlying architecture. The experimental results show a precision of 86.14% in terms of mAP. The work finds its application in South Asian contexts as more smart cities are formed in this region. The proposed framework will enable traffic monitoring with higher reliability, accuracy and granularity, contributing in having next-generation ITS.

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Section: Transfer Learningmentioning

confidence: 99%