Intelligent Object Detection System

As technology improved, object detection, which is connected to video and image analysis, caught researchers' interest. Earlier object recognition techniques are based on hand-crafted features and imprecise architectures and trainable algorithms. One of the main issues with many object detection systems is that they rely on other computer vision methods to support their deep learning-based methodology, which leads to slow and subpar performance. In this article, we present an end-to-end solution to the object detection problem using a deep learning based method. The single shot detector (SSD) technique is the quickest method for object detection from an image using a single layer of a convolution network. Our research's primary goal is to enhance accuracy of SSD method

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-30
DOI
https://doi.org/10.5281/zenodo.15770261
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
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article

Intelligent Object Detection System

Zenodo (CERN European Organization for Nuclear Research)
Advanced Neural Network Applications
article

Intelligent Object Detection System

article en

Abstract

As technology improved, object detection, which is connected to video and image analysis, caught researchers' interest. Earlier object recognition techniques are based on hand-crafted features and imprecise architectures and trainable algorithms. One of the main issues with many object detection systems is that they rely on other computer vision methods to support their deep learning-based methodology, which leads to slow and subpar performance. In this article, we present an end-to-end solution to the object detection problem using a deep learning based method. The single shot detector (SSD) technique is the quickest method for object detection from an image using a single layer of a convolution network. Our research's primary goal is to enhance accuracy of SSD method

Zenodo (CERN European Organization for Nuclear Research)
Dr. NTR University of Health Sciences (IN)
Openalex Percentile: Top 53%
Advanced Neural Network Applications
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