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@article{192676,
        author = {Chirag Chaudhari and Aniket Aher and Soham Salve and Prof. R. V. Deshpande},
        title = {Vision sort},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2141-2145},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192676},
        abstract = {VISION SORT is an intelligent automation system designed for accurate object counting and sorting using image segmentation and Generative Artificial Intelligence (AI). The system addresses limitations of traditional manual and sensor-based sorting techniques, such as low accuracy, high labor dependency, and limited adaptability. By leveraging computer vision techniques, segmented image analysis, and AI-based decision-making, the system can identify, classify, count, and sort objects in real time. Image segmentation is used to isolate individual objects from captured images, while Generative AI enhances robustness by learning variations in shape, size, color, and orientation. The proposed system improves efficiency, scalability, and precision in industrial and agricultural sorting applications. Experimental analysis demonstrates improved accuracy, reduced processing time, and minimal human intervention compared to conventional systems.},
        keywords = {Image Segmentation, Computer Vision, Generative AI, Automated Sorting, Object Counting, Smart Automation},
        month = {February},
        }