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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Sabapathy Ranganathan, Chitra
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Computer Science & IT Engineering

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Convolutional neural network based encoder-decoder for efficient real-time object detection Rajasekaran, Mothiram; Sabapathy Ranganathan, Chitra; Mohankumar, Nagarajan; Sampathrajan, Rajeshkumar; Merlin Inbamalar, Thayalagaran; Nandhini, Nageshvaran; Sujatha, Shanmugam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1960-1967

Abstract

Convolutional neural networks (CNN) are applied to a variety of computer vision problems, such as object recognition, image classification, semantic segmentation, and many others. One of the most important and difficult issues in computer vision, object detection, has attracted a lot of attention lately. Object detection validating the occurrence of the object in the picture or video and then properly locating it for recognition. However, under certain circumstances, such as when an item has issues like occlusion, distortion, or small size, there may still be subpar detection performance. This work aims to propose an efficient deep learning model with CNN and encoder decoder for efficient object detection. The proposed model is experimented on Microsoft Common Objects in Context (MS-COCO) dataset and achieved mean average precision (mAP) of about 54.1% and accuracy of 99%. The investigational outcomes amply showed that the suggested mechanism could achieve a high detection efficiency compared with the existing techniques and needed little computational resources.
Co-Authors Merlin Inbamalar, Thayalagaran Mohankumar, Nagarajan Nandhini, Nageshvaran Rajasekaran, Mothiram Sampathrajan, Rajeshkumar Sujatha, Shanmugam