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YOLO-Based Real-Time Object Detection Using Computer Vision for Robotics
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence (AI), Computer Vision
Abstract
Robots operating in dynamic environments require rapid and reliable perception to identify people, tools, obstacles, and other objects before making movement or manipulation decisions. Conventional vision pipelines may require separate stages for region proposal, classification, and localization, which can increase computational cost and latency. You Only Look Once (YOLO) formulates object detection as a single-network prediction task and has become a practical family of detectors for real-time computer-vision applications. This paper presents a research-oriented framework for integrating a lightweight YOLO detector with a robot-mounted camera. The framework covers image acquisition, preprocessing, object detection, confidence filtering, coordinate interpretation, and communication with a robot controller. It also discusses model selection, deployment constraints, evaluation metrics, safety considerations, and a reproducible testing plan. Rather than reporting unperformed experiments, the paper defines how a prototype should be evaluated using precision, recall, mean average precision, inference latency, frame rate, and end-to-end response time. The analysis indicates that compact YOLO variants are suitable starting points for resource-constrained robotic platforms, while reliable navigation requires additional depth or distance sensing and a safety controller. The proposed approach can support obstacle awareness, object following, pick-and-place assistance, and indoor mobile-robot perception.
Keywords
computer vision; object detection; robotics; real-time inference; YOLO
References
[1] Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016. p. 779–788. https://doi.org/10.1109/CVPR.2016.91
[2] Ultralytics. Object detection with Ultralytics YOLO. Ultralytics documentation. https://docs.ultralytics.com/tasks/detect/ (accessed 16 October 2026).
[3] Ultralytics. Explore Ultralytics YOLOv8. Ultralytics documentation. https://docs.ultralytics.com/models/yolov8/ (accessed 16 October 2026).
[4] Top A, Gökbulut M. Real-time deep learning-based position control of a mobile robot. Engineering Applications of Artificial Intelligence. 2024;138(Part A):109373. https://doi.org/10.1016/j.engappai.2024.109373
[5] Abood SH, Al-Khafaji HMH, Al-Khafaji MMH. Enhancing collision avoidance in mobile robots using YOLOv5: A lightweight approach for unstructured environments. Journal of Robotics and Control. 2025;6(2):769–778. https://doi.org/10.18196/jrc.v6i2.25856
How to cite this paper
@article{1724117,
author = {Aafareen Sikandar Shaikh, Archana Nilesh Pawar},
title = {YOLO-Based Real-Time Object Detection Using Computer Vision for Robotics},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {2721-2726},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724117.pdf},
abstract = {Robots operating in dynamic environments require rapid and reliable perception to identify people, tools, obstacles, and other objects before making movement or manipulation decisions. Conventional vision pipelines may require separate stages for region proposal, classification, and localization, which can increase computational cost and latency. You Only Look Once (YOLO) formulates object detection as a single-network prediction task and has become a practical family of detectors for real-time computer-vision applications. This paper presents a research-oriented framework for integrating a lightweight YOLO detector with a robot-mounted camera. The framework covers image acquisition, preprocessing, object detection, confidence filtering, coordinate interpretation, and communication with a robot controller. It also discusses model selection, deployment constraints, evaluation metrics, safety considerations, and a reproducible testing plan. Rather than reporting unperformed experiments, the paper defines how a prototype should be evaluated using precision, recall, mean average precision, inference latency, frame rate, and end-to-end response time. The analysis indicates that compact YOLO variants are suitable starting points for resource-constrained robotic platforms, while reliable navigation requires additional depth or distance sensing and a safety controller. The proposed approach can support obstacle awareness, object following, pick-and-place assistance, and indoor mobile-robot perception.},
keywords = {computer vision; object detection; robotics; real-time inference; YOLO},
month = {October},
}