IMPROVING SURVEILLANCE EFFICENCY WITH RESNET - 101  IN OBJECT DETECTION

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K.BARANE PRASATH; AROKIARAJ CHRISTIAN ST HUBERT; PRAVEEN KUMAR E; PUNITHAN M

IMPROVING SURVEILLANCE EFFICENCY WITH RESNET - 101 IN OBJECT DETECTION

Authors: K.BARANE PRASATH, AROKIARAJ CHRISTIAN ST HUBERT, PRAVEEN KUMAR E, PUNITHAN M

Unique Paper ID: 175482
Volume: 11
Issue: 11
PageNo: 3375-3383

Keywords: Security Real-time Surveillance Threat Detection RCNN ResNet-101 Deep Learning Residual Connections Object Detection Accuracy Speed.

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.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 3375-3383

IMPROVING SURVEILLANCE EFFICENCY WITH RESNET - 101 IN OBJECT DETECTION

Available:https://ijirt.org/article?manuscript=175482

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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