Android_YOLO

  Architecture Overview of TensorFlow Lite              TensorFlow  Lite support both Android and iOS platforms.  The initial step involves conversion of a trained TensorFlow model to TensorFlow Lite file format (.tflite) Using the TensorFlow Lite Converter. This converted model file is used in the application. Following are the important components for deploying the model as shown in the architecture diagram: Java API: A wrapper around the C++ API (For Android) C++ API: The C++ API is responsible for loading the model file and invoking the interpreter for further processing and execution. The same library is used for android and iOS. Interpreter: Interpreter executes the model using the defined kernels. The interpreter supports selective kernel loading developers can also define their own custom kernel that can be used perform the execution.  On few android devices, the interpreter uses the Android Neural Network API for hardw...

             Research shows that the detection of objects like a human eye has not been achieved with high accuracy using cameras and cameras cannot be replaced with a human eye. Detection refers to identification of an object or a person by training a model by itself. Detection of images or moving objects have been highly worked upon, and has been integrated and used in commercial, residential and industrial environments. But, most of the strategies and techniques have heavy limitations in the form of computational resources, lack of proper data analysis of the measured trained data, dependence of the motion of the objects, inability to differentiate one object from other, and also there is a concern over speed of the movement and Illuminacy. Hence, there is a need to draft, apply and recognize new techniques of detection that tackle the existing limitations. Scope              Object Detection is a ve...