A survey of target detection algorithms in deep learning

Tao Yu; Jianqiao Ren; Xueling Chang; Xiaozhen Zhu; Yu Xie

doi:10.1117/12.3011521

7 December 2023 A survey of target detection algorithms in deep learning

Tao Yu, Jianqiao Ren, Xueling Chang, Xiaozhen Zhu, Yu Xie

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129410S (2023) https://doi.org/10.1117/12.3011521
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

The application of deep learning techniques significantly improves the performance of target detection algorithms. The article provides a comprehensive discussion of target detection principles, methods, algorithms, and future trends. First, the principles of target detection algorithms are summarized, including tasks, evaluation indexes, and public datasets. The image-based target detection algorithms are divided into two levels: traditional target detection and deep learning-based target detection. A brief overview of the types and shortcomings of traditional target detection algorithms is provided. Deep learning-based target detection is divided into single-stage and dual-stage for specific description. It summarizes the mechanisms, advantages, and limitations of each type of algorithm, conducts a comparative analysis, and suggests the future research direction of deep learning-based target detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tao Yu, Jianqiao Ren, Xueling Chang, Xiaozhen Zhu, and Yu Xie "A survey of target detection algorithms in deep learning", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129410S (7 December 2023); https://doi.org/10.1117/12.3011521

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