A comprehensive review of object detection with traditional and deep learning methods

Vrushali Pagire, Murthy Chavali, Ashish Kale,
A comprehensive review of object detection with traditional and deep learning methods,
Signal Processing,
Volume 237,
2025,
110075,
ISSN 0165-1684,
https://doi.org/10.1016/j.sigpro.2025.110075.
(https://www.sciencedirect.com/science/article/pii/S0165168425001896)
Abstract: Object detection is one of the most important and challenging tasks of computer vision. It has numerous applications in the fields of agriculture, defence, retail markets and manufacturing units, transportation, social media platforms, medical, wildlife monitoring and conservation. This survey aims to give researchers a comprehensive understanding of the current state of object detection algorithms. In this review, object detection and its different aspects have been covered in detail. This review paper starts with a quick overview of object detection followed by traditional and deep learning models for object detection. The section on deep learning models provides a comprehensive overview of one-stage and two-stage object detectors. A detailed discussion is given of the transformer-based detectors and lightweight networks category. Additionally, the evaluation metrics used for object detection methods are discussed systematically. The best object detection algorithms for different applications are discussed at the end of the survey. This survey is useful for beginners who want to study different object detection algorithms and their use in different applications.
Keywords: Classification; Feature extraction; Deep learning; Lightweight networks; Object detection; Traditional methods