The increasing need for advanced security systems has led to the development of various intelligent identification and detection techniques. This research proposes a smart identification and detection system that distinguishes between living and non-living things for enhanced security. The proposed system uses deep learning techniques for feature extraction and classification of images. The security system trains a deep Convolutional Neural Network on the ImageNet dataset, which is a large collection of labeled images of various objects and living beings. The trained model is then used to identify and detect living and non-living things in real-world scenarios. The proposed system shows promising results in accurately distinguishing between living and non-living objects, which can be used to enhance security in various domains such as surveillance, border control, and access control. This system implements and simulates the proposed model using the TensorFlow framework, which is a widely used open-source library for building and training deep learning models. The model is trained on a large dataset of images, using various optimization techniques to improve the accuracy of the predictions. The trained model is then deployed in a real-world scenario to detect living and non-living objects with high accuracy. The proposed system can provide enhanced security in various domains where the detection of living and non-living objects is crucial. The system can be used to detect intruders, unauthorized access, and other security threats. Additionally, the proposed system can be integrated with other security systems to provide a comprehensive security solution. Overall, the proposed smart identification and detection system can significantly improve security systems in various domains, making them more efficient and reliable.

Smart security Deep Learning IoT Microcontroller TensorFlow CNN