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@article{193764,
        author = {K.R. Rohith Kumar and K.Shanmugam and M. Jagadeesh and B. Bharath Simha Reddy and K. Madhan and K.Kartheek Ganesh},
        title = {Advanced Object Detection in Real-Time Drone Surveillance Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1433-1440},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193764},
        abstract = {Deep learning algorithms, particularly convolutional neural networks (CNNs), have shown remarkable promise in improving the accuracy and efficiency of object detection in UAV-based remote sensing applications. These methods are capable of processing large volumes of aerial imagery in real time, which is crucial for tasks such as surveillance, monitoring, and disaster response. The research explores the comparison between one-stage detectors, which offer faster processing speeds, and two-stage detectors, which provide higher accuracy but at the cost of speed. Further, the integration of deep learning with UAVs is assessed, focusing on software advancements that enable seamless real-time object detection. The paper concludes by highlighting challenges, such as computational limitations, and suggesting potential solutions to enhance performance in dynamic environments.},
        keywords = {YOLO, SSD, Feature Pyramid Networks (FPN), Transfer Learning, Deep Learning, UAV Surveillance, Real-time Detection.},
        month = {March},
        }