Application of Deep Learning in Robot Object Detection

DOI

10.2991/978-94-6463-823-3_47How to use a DOI?

Keywords

Object Detection; Deep Learning; Robotics; Environment Perception

Abstract

Object detection is the core technology of robot environment perception. The introduction of deep learning has significantly enhanced its performance, marking a qualitative leap. This paper systematically reviews the development of object detection algorithm based on CNN. From the early R-CNN to the latest YOLOv8, the detection speed increased 7,400 times, while the accuracy rate has shown a steady rise from 53.3% to 57.9%, reflecting consistent optimization. Compared with traditional methods (such as HOG, DPM), CNN algorithm shows significant advantages in accuracy, real-time and environmental adaptability (lighting changes, occlusion scene error reduction by 50–70%). In practical applications such as UAV power inspection and service robot obstacle avoidance, these algorithms achieve more than 98% detection accuracy and millisecond delay, providing key technical support for robot intelligence. In the future, with the development of Transformer architecture and edge computing optimization, object detection technology will further promote the application of robots in complex scenarios.

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  - Boning Zhao
PY  - 2025
DA  - 2025/08/31
TI  - Application of Deep Learning in Robot Object Detection
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 476
EP  - 483
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_47
DO  - 10.2991/978-94-6463-823-3_47
ID  - Zhao2025
ER  -