Abstract:
The training and deployment of YOLOv4 and YOLOv5 models in an Android application play a vital role in achieving real-time object detection on mobile devices. This abstract overviews the critical steps in training and integrating these models into an Android application. The training process for YOLOv4 and YOLOv5 begins with collecting and annotating a labelled dataset, where bounding box coordinates and object classes are assigned to the images. Deep learning techniques are then applied to train the models, optimizing their parameters through iterative processes to improve object detection accuracy. PyTorch and TensorFlow are commonly employed for training YOLOv4 and YOLOv5 models offer comprehensive support for model architecture design, data preprocessing, and optimization algorithms. Once trained, the models must be deployed in an Android application. This involves converting the trained models into a format compatible with mobile devices like TensorFlow Lite. TensorFlow Lite enables the efficient execution of deep learning models on Android smartphones and other resource-constrained devices. The loaded YOLOv4 or YOLOv5 model is utilized in the Android application for real-time object detection. The application captures frames from the device’s camera, feeds them into the model, and receives predictions through bounding boxes and class labels. These predictions are then superimposed on the camera feed, providing the user a real-time object detection experience. To optimize the performance of the models in the Android application, techniques like model quantization and hardware acceleration can be employed. Model quantization reduces memory and computation requirements, leading to faster inference on mobile devices. Hardware acceleration, such as utilizing the GPU, further enhances the speed and efficiency of the object detection process. In conclusion, training YOLOv4 and YOLOv5 models involve dataset collection, annotation, and deep learning model training, followed by deployment in Android applications using frameworks like TensorFlow Lite. Integrating these models into an Android application enables real-time object detection, enhancing the functionality and usability of various applications, including augmented reality and image recognition.
