International Journal on Science and Technology

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"An Efficient Object Detection Using Faster R-CNN"

Author(s) SUREKHA ASWALE, DR.RAJU SAIRISE
Country India
Abstract This project presents an efficient object detection algorithm in digital images. Traditional object detection methods, particularly those based on deep learning, offer high accuracy but are often resource-intensive, making them unsuitable for deployment on low-power or real-time embedded systems. To address these chal- lenges, the proposed algorithm focuses on object detection using Otsu threshold- ing, followed by a window-based scanning mechanism and Faster R-CNN boundary expansion technique to accurately identify and isolate object boundaries. The al- gorithm is implemented using Python and Open CV, and tested on real-world datasets, such as traffic images containing closely packed vehicles. It offers a tun- able trade-off between detection resolution and processing time, making it adapt- able for various application needs. The system operates independently of object shape or size, and provides a robust solution for environments where computa- tional resources are limited. Experimental results demonstrate the effectiveness of the approach in achieving accurate detection with minimal overhead. This makes the algorithm well-suited for applications such as traffic surveillance, industrial sorting, and smart agriculture, with future work focused on enhancing robustness to lighting variations and extending support for object tracking in video streams.
Keywords Otsu thresholding,Contour detection,Fatser R-CNN,Python,Open CV.
Field Engineering
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-06-26
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