Comparative Analysis of Object Sorting Algorithms Based on Size and Color Using Image Processing Techniques

This research paper presents a comparative analysis of different image processing software’s performance in the context of physical object sorting. The objective of this project is to develop a system that can accurately and efficiently sort objects based on their sizes and colors. The implemented code in Python serves as a benchmark for evaluating the advantages and features offered by various object detection models including YOLO, OpenCV, SSD, Faster R-CNN, RetinaNet, Mask R-CNN, EfficientDet, and Cascade R-CNN. This analysis aims to identify the novelty features and assess the effectiveness of each software to determine the most suitable approach for object sorting using image processing techniques.