<![CDATA[The development of Computer Vision technology in recent years has shown significant growth along with advances in Deep Learning methods and the availability of large-scale datasets. Numerous studies have produced various approaches, architectures, and evaluation metrics, creating the need for a structured mapping to comprehensively understand the direction of this field. This study aims to analyze methodological trends, research task focuses, and dominant evaluation metrics in Computer Vision research. The method employed is a Systematic Literature Review (SLR) of 20 scientific article published between 2017 and 2025. The analysis process was conducted through data extraction covering method types, task focuses, datasets, and evaluation metrics used in each study. The results indicate that Convolutional Neural Networks and Vision Transformers are the most dominant architectures, with the primary research focuses on object detection, image classification, and video understanding. The most frequently used evaluation metrics are accuracy, mean Average Precision (mAP), and Intersection over Union (IoU). These findings reveal a gradual shift from convolution-based approaches toward transformer-based architectures that are more adaptive to large-scale visual data. This study provides a comprehensive overview of the development direction of Computer Vision and can serve as a reference for future research in selecting relevant methods and research focuses, both in terms of accuracy-oriented performance and computational efficiency.]]>
Copyrights © 2026
