2D Object Detection: A Survey

Emanuele Malagoli () and Luca Di Persio
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Emanuele Malagoli: Department of Information Engineering and Computer Science (DISI), University of Trento, 38123 Trento, Italy
Luca Di Persio: Department of Computer Science, University of Verona, 37134 Verona, Italy

Mathematics, 2025, vol. 13, issue 6, 1-52

Abstract: Object detection is a fundamental task in computer vision, aiming to identify and localize objects of interest within an image. Over the past two decades, the domain has changed profoundly, evolving into an active and fast-moving field while simultaneously becoming the foundation for a wide range of modern applications. This survey provides a comprehensive review of the evolution of 2D generic object detection, tracing its development from traditional methods relying on handcrafted features to modern approaches driven by deep learning. The review systematically categorizes contemporary object detection methods into three key paradigms: one-stage, two-stage, and transformer-based, highlighting their development milestones and core contributions. The paper provides an in-depth analysis of each paradigm, detailing landmark methods and their impact on the progression of the field. Additionally, the survey examines some fundamental components of 2D object detection such as loss functions, datasets, evaluation metrics, and future trends.

Keywords: object detection; deep learning; convolutional neural networks; transformer; computer vision (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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