METHODS AND ALGORITHMS FOR IDENTIFYING DANGEROUS OBJECTS FOR ROBOTIC DEVICES BASED ON ARTIFICIAL INTELLIGENCE
A.A. Khakimov4th year student, Samarkand State University named after Sharof Rashidov, Samarkand, UzbekistanF. M. NazarovD.Sc., Assoc. Prof., Dean of the Faculty of Artificial Intelligence and Digital Technologies, Samarkand State University named after Sharof Rashidov, Samarkand, UzbekistanI. Musurmonova1st year student, Samarkand State University named after Sharof Rashidov, Samarkand, Uzbekistan
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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.
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