Progress on Deep Learning-Driven Target Detection Techniques

DOI

10.2991/978-94-6463-821-9_69How to use a DOI?

Keywords

Deep learning; target detection; artificial intelligence; machine vision

Abstract

Deep learning (DL)-driven target detection techniques are one of the key topics in today’s research. The target detection algorithms have been updated along with researchers’ continuous updating of target detection model libraries and datasets in recent years. However, there is a lack of knowledge about the technical route and key performance of target detection techniques driven by DL. Therefore, this paper explores the research progress of DL-driven target detection techniques by collecting and analyzing the iterative algorithms of DL-driven target detection techniques for the past few years. This paper reviews the latest research advances of this technology to discuss its strengths and weaknesses, as well as a prediction of its challenges and possible future directions. It has been found that the two-stage target detection algorithm is much more accurate in processing images, yet it is too slow and is suitable for use in situations where a high standard of accuracy in image processing is required. The single-stage target detection algorithm is more suitable for applications requiring large-scale pipelined processing. This paper can provide technical guidance or theoretical support for DL applications in areas such as manufacturing.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Dinghuizhi Zhang
PY  - 2025
DA  - 2025/08/31
TI  - Progress on Deep Learning-Driven Target Detection Techniques
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 719
EP  - 729
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_69
DO  - 10.2991/978-94-6463-821-9_69
ID  - Zhang2025
ER  -