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All Journal Journal La Multiapp
Al-wakel, Esraa Hassan Jawad
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering

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Custom Object Detection Using Transfer Learning with Pretrained Models for Improved Detection Techniques Mtasher, Ashwaq Katham; Al-wakel, Esraa Hassan Jawad
Journal La Multiapp Vol. 5 No. 1 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i1.843

Abstract

Custom object detection plays a vital role in computer vision applications. However, developing an accurate and efficient custom object detector requires a substantial amount of labeled training data and significant computational resources. In this research, we propose a custom object detection framework that leverages transfer learning with pre-trained models to improve detection tech-niques.The framework first utilizes a pre-trained deep learning model, such as ResNet or VGGNet, as a feature extractor. The pre-trained model is trained on a large-scale dataset, enabling it to learn high-level features from various objects. By reusing the pre-trained model's convolutional layers, we effectively capture generic features that can be transferred to the custom object detection task.Experimental evaluations on benchmark datasets demonstrate the effectiveness of our ap-proach. The custom object detector achieved superior detection performance compared to tradi-tional methods, especially when the target objects have limited training data. Additionally, our framework significantly reduces the amount of training time and computational resources required, as it leverages pre-trained models as a starting point.
Co-Authors Mtasher, Ashwaq Katham