Title: A fast R-CNN-based novel and improved object recognition technique

Authors: Shriram K. Vasudevan; R. Sargunan; G.I. Aswath; K. Vimal Kumar

Addresses: K. Ramakrishnan College of Technology, Kariyamanikam Road, Samayapuram, Trichy – 621112, India ' Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India ' Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India ' Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

Abstract: Humans have the natural power to identify objects easily on their own, but a machine cannot. An algorithmic description of recognition task has to be implemented on machines to identify an object in an image. A very challenging and a tough task in the computer vision are to detect objects and to estimate the pose. Object recognition remains to be one such key feature in computer vision technology and it is used for identifying a specific object in a digital image or video. The importance of object recognition algorithm is very high in real-world applications. Object detection is much more complex and challenging compared to image classification. Some of the applications include Biometric recognition, industrial inspection, robotics, intelligent vehicle system, human-computer interaction, etc. In a retail business, identifying the products of a single manufacturer is difficult. This research uses fast R-CNN algorithm to detect the products of a particular manufacturer (Procter & Gamble).

Keywords: object recognition; deep learning; machine learning; convolutional neural network; CNN; region-based convolutional neural network; R-CNN; fast R-CNN.

DOI: 10.1504/IJAIP.2025.147915

International Journal of Advanced Intelligence Paradigms, 2025 Vol.30 No.4, pp.321 - 333

Received: 26 Jan 2019
Accepted: 20 Feb 2019
Published online: 08 Aug 2025 *

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