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

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

Anand Magar, Dhruv Sharma ,Dhruv Mulay, Dhruv Chaudhari, Dhriti Nair, Vinit Dhoot

Department of Multidisciplinary Engineering

Vishwakarma Institute of Technology, Pune, 411037, Maharashtra, India

Abstract - 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.

Keywords — Cascade R-CNN, Color-based sorting, Comparative analysis, Computational efficiency, EfficientDet, Faster R-CNN, Image processing, Mask R-CNN, Object detection, Object sorting techniques, OpenCV, Physical object sorting, RetinaNet, Size-based sorting, SSD