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Methods and Algorithms for Identifying Dangerous Objects for Robotic Devices Based on Artificial Intelligence

Year: 2026

Type: article

Abstract: This study explores the development of methods and algorithms for detecting hazardous objects in robotic systems powered by artificial intelligence. A specialized dataset named DENGROUS was created using the Roboflow platform, consisting of 10 classes, 15,668 images, and 21,944 annotations. Their performance was then comparatively evaluated on a validation dataset. Experimental results showed that YOLO26s achieved the highest accuracy with [email protected] = 0.876 and [email protected]:0.95 = 0.701. The YOLO11s model ranked second with a Precision score of 0.897, while YOLOv8n demonstrated the fastest inference speed at 4.5 ms per image.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20116527

Authors: A.A. Khakimov, F.M. Nazarov, I. Musurmonova

Institution: Samarkand State University named after Sharof Rashidov

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7160846789

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