Analytical Study and Recommendations for Computer Vision Methods

The purpose of this comparative study is to determine the effectiveness of various computer vision techniques for image analysis tasks. This research aims to investigate different computer vision models for object detection and recognition. Using computer vision models for object detection and recognition, this research aims to replace the cumbersome manual system currently in use. This is accomplished by comparing popular models such as YOLO and Haar Cascade based on their precision, speed, and efficiency. In addition, facial recognition methods, such as the Siamese Model and the face-recognition library, are analyzed based on their performance using metrics such as time, and similarity scores. The challenges and limitations of each approach are examined to identify the most suitable model for specific tasks. As a proof of concept, this study concludes by implementing its recommendation on a popular use case.