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@article{175482,
        author = {K.BARANE PRASATH and AROKIARAJ CHRISTIAN ST HUBERT and PRAVEEN KUMAR E and PUNITHAN M},
        title = {IMPROVING SURVEILLANCE EFFICENCY WITH RESNET - 101  IN OBJECT DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3375-3383},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175482},
        abstract = {Security remains a global challenge, requiring advanced mechanisms for real-time threat detection. Traditional object detection models like RCNN struggle with accuracy and speed, limiting their effectiveness in surveillance applications. ResNet-101, a deep learning-based convolutional neural network, addresses these issues with its residual connections, enabling deeper network training without vanishing gradients. This architecture enhances feature extraction, improving accuracy while maintaining computational efficiency. Compared to RCNN, ResNet-101 processes images faster, making it suitable for real-time surveillance. Its ability to detect threats with higher precision ensures timely responses, reducing potential security risks. By leveraging ResNet-101, surveillance systems can achieve superior threat identification, enhancing safety across various environments. This advancement in deep learning significantly improves real-time monitoring, making security infrastructure more reliable and responsive. As security threats evolve, adopting efficient models like ResNet-101 becomes essential for robust and proactive surveillance solutions.},
        keywords = {Security, Real-time Surveillance, Threat Detection, RCNN, ResNet-101, Deep Learning, Residual Connections, Object Detection, Accuracy, Speed.},
        month = {April},
        }