Review of Object Detection

DOI: https://doi.org/10.62517/jike.202604207

Author(s)

Jiwei Ye

Affiliation(s)

School of Intelligent Manufacturing, Hebei Polytechnic University of Industry, Shijiazhuang, China

Abstract

Object detection, as a fundamental and critical problem in the field of computer vision, holds significant theoretical research value and demonstrates extensive industrial application value in numerous practical scenarios. This technology has been applied across various domains, including facial recognition, industrial defect detection, and remote sensing detection. Based on a comprehensive review of relevant domestic and international literature, this paper presents a survey of object detection techniques. It begins by outlining the key developmental stages of object detection technology. Subsequently, it discusses aspects such as datasets, evaluation metrics, and performance analysis, classifies existing object detection algorithms, and analyzes and compares the performance of different algorithms on datasets. Finally, the paper provides an outlook on future research directions in object detection.

Keywords

Object Detection; Convolutional Neural Network; Deep Learning

References

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