A Review of Object Detection Based on Convolutional Neural Networks and Deep Learning

Main Article Content

MingYuan Wang
Watis Leelapatra

Abstract

   Object detection, as one of the three main tasks of computer vision, is of great importance for the development of artificial intelligence in the future. The rapid advancement of convolutional neural networks (CNNs) and deep learning have provided a broader arena for object detection. From traditional methods to state-of-the-art algorithms, numerous innovative technologies and methods have been proposed. This paper reviews the one-stage and two-stage object detection algorithms and compares their advantages and shortcomings from various aspects. Some applications in real life, such as self-driving, weeding robots are illustrated in this paper. Finally, current issues and future studies direction are prospected.

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Wang, M., & Leelapatra, W. (2022). A Review of Object Detection Based on Convolutional Neural Networks and Deep Learning. INTERNATIONAL SCIENTIFIC JOURNAL OF ENGINEERING AND TECHNOLOGY (ISJET), 6(1), 1–7. Retrieved from https://ph02.tci-thaijo.org/index.php/isjet/article/view/245969
Section
Research Article

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