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Overview of Object Detection Methods

Jingbin Yang, Xiumin Shi, Sanpeng Deng, Yuming Qi
International Journal of Computer Science and Information Technology, (2026), Vol.8, No.1, pp.99-105
Published: January 11, 2026
DOI: 10.62051/ijcsit.v8n1.11
PDF: Download Full Text PDF
Abstract

Object detection is an important task in computer vision, aimed at detecting and recognizing the position and category of target objects from images or videos. With the rise of deep learning, the accuracy and efficiency of object detection have significantly improved, especially the application of convolutional neural networks (CNN) in this field, which has made significant breakthroughs in object detection methods. This article provides an overview of the development history of object detection, with a focus on classic object detection algorithms, deep learning methods, and their evolution. It explores the evaluation criteria and challenges faced by object detection, and looks forward to future development trends.

Keywords: Object detection, Deep learning, Convolutional neural networks, YOLO, Faster R-CNN, Evaluation Criteria1
APA Citation: Jingbin Yang, Xiumin Shi, Sanpeng Deng, Yuming Qi (2026). Overview of Object Detection Methods. International Journal of Computer Science and Information Technology, 8(1), 99-105. https://doi.org/10.62051/ijcsit.v8n1.11

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