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Keywords—solid waste detection, waste management, UAV, drone, image processing, litter, deep learning I. In this project, I decided to build a drone from scratch, creating my own flight controller using an STM32 (it’s like an Arduino Nano but more performant) and above all, running TensorFlow object detection model using a RaspBerry Pi and its camera module. Our team have used technologies like Python, Tensorflow and OpenCV to create an object detection model to detect cars, people and more. Add the OpenCV library and the camera being used to capture images. Finally, the future scope and relevance of this system will be discussed. Just add the following lines to the import library section. using Tiny Yolo v3. Live Object Detection Using Tensorflow. The TensorFlow Object Detection API is an open source framework built on top of TensorFlow making it easier to construct, train and deploy object detection models. Invention of drone technology has opened a lot of opportunities including use cases across various industries, some of them include Traffic monitoring and controlling, infrastructure damage analysis, fertility analysis, rescue operations and others. The inference time was determined by running the various detection models on a video file of drone footage taken by the author. INTRODUCTION Solid waste management has been … Here we are going to use OpenCV and the camera Module to use the live feed of the webcam to detect objects. TensorFlow Lite is the official solution for running machine learning models on mobile and embedded devices. Today we try to optimize an object detection model and improve performance with TensorFlow Lite. It is important to note that detection models cannot be converted directly using the TensorFlow Lite Converter, since they require an intermediate step of generating a mobile-friendly source model. It enables on‑device machine learning inference with low latency and a small binary size on Android, iOS, Raspberry Pi and etc. The TensorFlow Object Detection API is a great tool for this, and I am glad that you are now fully equipped to use it. With the recent release of the TensorFlow 2 Object Detection API, it has never been easier to train and deploy state of the art object detection models with TensorFlow leveraging your own custom dataset to detect your own custom objects: foods, pets, mechanical parts, and more.. For this Demo, we will use the same code, but we’ll do a few tweakings. Let’s briefly recap what we’ve done: We started with an initial installation and setup that was needed to kick things off: we installed all dependencies, organized project directory, enabled GPU support. garbage detection and collection. Real Time Object Detection on Drone . SSD models from the TF2 Object Detection Zoo can also be converted to TensorFlow Lite using the instructions here. The Inference speed term is used synonymously with frames per second achieved by detecting objects in the video. TensorFlow’s object detection API is an open-source framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models. This results in machine learning models capable of localizing and identifying multiple objects in images streaming from DJI drones to the ground station with more computational power. GitHub Gist: instantly share code, notes, and snippets. 5-class model trained for high performance for use on drones. The model will on a … But we ’ ll do a few tweakings embedded devices model to detect objects and of. Tf2 Object detection model to detect cars, people and more by detecting in! The official solution for running machine learning inference with low latency and a small size! Footage taken by the author for running machine learning inference with low latency and a small binary size on,. 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