Design and Development of Object Detection Using Tensor Flow Lite and Deep Learning Approaches in Data Mining

Abstract

Object detection is a challenging computer vision task that involves identifying and locating objects in images or videos. Convolutional neural networks (CNNs) are a powerful deep learning technique that can be used for object detection. CNNs are able to learn to extract features from images that are relevant to object detection, such as edges, shapes, and textures. Tensor Flow Lite is a lightweight version of the TensorFlow framework that is designed for mobile and embedded devices. This makes it ideal for object detection applications that need to run on devices with limited resources, such as smartphones and drones. The model is then optimized and converted to Tensor Flow Lite format, which enables efficient deployment on mobile devices. The proposed system is evaluated on a benchmark dataset, and the results show that it achieves high accuracy while maintaining real-time performance on mobile devices. The system has potential applications in various fields, including robotics, autonomous driving, and surveillance systems.