Automatization of object detection with AI
| dc.contributor.author | Ulykbek A. | |
| dc.date.accessioned | 2025-04-16T10:08:39Z | |
| dc.date.available | 2025-04-16T10:08:39Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | This study performs a comprehensive analysis of the YOLO (You Only Look Once) object detection method, painstakingly evaluating its performance on a wide range of image formats. The investigation’s main focus is on critical metrics that are carefully examined across set of different images, including processing time, frames per second (FPS), and important metrics related to object detection. By means of this rigorous examination, the research reveals noteworthy variations in the algorithm’s effectiveness, illuminating its intrinsic merits and demerits under various circumstances and situations.These results provide a vital source of information for practitioners and researchers working in the field of real-time object recognition applications. They enable them to make informed decisions and create optimization plans specifically for YOLO-based systems. This study provides stakeholders with the tools and considerations needed to efficiently negotiate the complexity of real-world deployments by providing a detailed understanding of the algorithm’s performance peculiarities, thereby promoting improvements and innovation in the field of computer vision. | |
| dc.identifier.citation | Ulykbek A / Automatization of object detection with AI / 2024 / 7M06102 - Computer Science | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1716 | |
| dc.language.iso | en | |
| dc.publisher | Faculty of Engineering and Natural Science | |
| dc.subject | Object Detection YOLO(You Only Look Once) Convolutional Neural Network (CNN) Single Shot Detector (SSD) FPS(Frames per Second) | |
| dc.title | Automatization of object detection with AI | |
| dc.type | Thesis |