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image of Vehicle Detection through Self-supervised Learning: An In-depth Review and Critical Analysis

Abstract

The application of computer vision such as monitoring traffic, surveillance, autonomous driving, and vehicle detection, is a crucial task. Traditionally, vehicle detection has been addressed using methods based on supervised learning that involve a huge quantity of labelled data. However, collecting and annotating huge amounts of data is expensive and time-consuming, leading researchers to explore methods based on supervised learning that learn from unlabelled data. The advanced techniques for vehicle identification utilizing self-supervised learning are thoroughly reviewed and critically analysed in this paper. We start by defining self-supervised learning and outlining its benefits and drawbacks in comparison to supervised learning. Then, we go through the variety of techniques based on a self-supervised learning approach for vehicle identification, including various pretext tasks, network structures, and training approaches that have been put out in the literature. In this article, we review recent developments in self-supervised learning for vehicle identification, covering well-liked pretext problems, network designs, and training methods. Furthermore, we critically analyse the strengths and limitations of these methods, highlighting their practical implications and potential research directions. Researchers and practitioners interested in creating reliable and effective vehicle detection systems utilizing self-supervised learning might use the information presented in this study as a reference. This review paper examines self-supervised learning techniques for vehicle detection, addressing the limitations of traditional supervised methods that require extensive labeled data. It covers various self-supervised approaches, including pretext tasks, network architectures, and training strategies. The paper critically analyzes these methods, discussing their strengths, limitations, and practical applications in traffic monitoring, surveillance, and autonomous driving. By evaluating current techniques and identifying future research directions, this review provides a comprehensive resource for researchers and practitioners developing efficient vehicle detection systems using self-supervised learning.

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2025-01-06
2025-07-30

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Vehicle Detection through Self-supervised Learning: An In-depth Review and Critical Analysis
Bentham Science Publishers ; https://doi.org/10.2174/0123520965318257241119081355
/content/journals/raeeng/10.2174/0123520965318257241119081355
/content/journals/raeeng/10.2174/0123520965318257241119081355
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  • Article Type:     Review Article
Keywords: CNN ; self-supervised learning ; environmental conditions ; feature learning ; fine-tuning ; real-world applications ; object detection ; computer vision ; image processing ; performance evaluation ; unsupervised learning ; autonomous vehicles ; transfer learning ; pre-training ; deep learning ; Vehicle detection

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