FGRCNN: A Newly Developed Hybrid Network for Multiple Object Detection Using Traffic Images

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 8

Year of Publication : 2025

Authors : Dhanalakshmi Manickaraj, Palvinder Singh Mann

10.14445/23488549/IJECE-V12I8P108

How to Cite?

Dhanalakshmi Manickaraj, Palvinder Singh Mann, "FGRCNN: A Newly Developed Hybrid Network for Multiple Object Detection Using Traffic Images," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 8, pp. 91-101, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I8P108

Abstract:

Detection of more than one object from traffic images in real time using a Social GAN-based Fast Regional Convolutional Neural Network is presented in this paper. To detect the objects, the images obtained from the Multi-Object Tracking (MOT) dataset had undergone pre-processing by employing a Gaussian filtering technique, and enhanced images were subjected to object detection by merging two networks, Social GAN and Fast Region-based CNN (FRCNN). To assess the efficiency of the approach, experimental tests were applied on diverse image datasets, and the results were validated. The Social-Gan-based FRCNN provided better accuracy of 86.5%, TPR of 94.7%, TNR of 90.9%, PPV of 88.8% and NPV of 55.8%.

Keywords:

Multiple object detection, Traffic images, Fast regional based social GAN.

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