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@article{173123,
        author = {Kunal Kumar Singh},
        title = {Enhancing the Real-Time Object Detection Using Fuzzy Boundary Detection in Image Segmentation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2485-2496},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173123},
        abstract = {Real-time object detection is one of the fundamental tools in computer vision applications, which range from surveillance operations to autonomous vehicles and robotic systems. The biggest challenge in object detection is accurate object detection and segmentation in cluttered dynamic environments that experience obscurations and lighting changes as well as scale changes. The presented work introduces fuzzy boundary detection that enhances the accuracy of object segmentation techniques to overcome the existing limitations in detection. The model is able to obtain a better edge definition for objects using the application of fuzzy c-means clustering which enhances its capability to separate objects from environmental noise. The pixel-level classification also becomes more accurate through this method because it applies fuzzy membership degrees to pixels that help in accurately identifying complex object boundaries. The performance level of the model gains significant improvements with both segmentation and classification operations when integrated with YOLOv5 object detection. The proposed model outperforms the baseline YOLOv5, along with other models, with the improvement of detection and segmentation accuracy due to better precision metrics that include mAP from 90.2% to 94.6%, mAP@0.5:0.95 from 79.1% to 85.4%, and IoU from 87.6% to 91.4%.},
        keywords = {Object Detection, Image Segmentation, Fuzzy Boundary Detection, YOLOv5 model.},
        month = {February},
        }