Real-time Detection and Classification using YOLOv4-Tiny


Traffic detection and classification is one of the important steps toward building a self-driving vehicle or intelligent autonomous vehicle. It is also important that such detection algorithms must be deployed on embedded computers such as in cars. There are two primary tasks for any recognition system, detection (finding the location and size of the object on the input image), and classification (classifying the detected objects into subclasses). Both tasks are usually done with a single detection/classification model such as YOLO or SSD, where input images are labelled with the bounding boxes and respective classes. However, labelling and training…


Introduction and Project Background

Artificial intelligence is now widely being used to perform several tasks that were previously performed by a human. An artificially created network can be trained to autonomously drive a vehicle, without any human intervention, creating a safer driving environment. These vehicles are equipped with different hardware such as camera, radar, and LIDAR, to collect the data in real-time. The data is then passed into complex neural networks to predict outcomes and control the car. Such networks can be built, train, and tested in a 3D realistic environment, without putting anyone on the risk. A simple self-driving vehicle…

Alzaib Karovalia

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