It’s good to have a basic knowledge of deep learning computer vision area.įor simplicity we will assume everything is installed. Familiar with the Python and Linux command line, a shell like bash, and an editor like nano.I recommend to use VScode IDE jointly with Remote Development using SSH extension. To run the NVIDIA Jetson board headless(without the monitor), set up either SSH access or RDP connection from your laptop.Nvidia Jetson Xavier NX Developer Kit (You could also use the Nvidia Jetson Nano, which is slightly cheaper and consumes less energy).Let me briefly talk about the prerequisites that are essential to proceed towards your own object detector: Running inference on test images and videos.Annotation conversation from PASCAL VOC format to YOLO.This guide will walk you through the process of training an object detection model. YOLOv7 is the latest versions of the YOLO family. It’s famous for being very accurate and fast at the same time. You only look once or YOLO is a state of the art object detection algorithm. Here, we are going to use Yolo-V7 to train our custom object detection model. There are various object detection algorithms out there like YOLO (You Only Look Once), Single Shot Detector (SSD), Faster R-CNN, Histogram of Oriented Gradients (HOG), etc. In this blog we will see how we can implement Safety Helmet Detection System using Computer Vision techniques, in particular through Object detection in order to detect if a worker is wearing his helmet or not, and reduce the number of accidents for the lack of a safety helmet.Īn object detection model is a machine learning algorithm that has learned to recognize and locate objects in images and videos. Head injuries can result in short- and long-term effects such as: Concussions, Memory loss, Brain damage, Fatality, etc. The main safety equipment of people in industrial places is the safety helmet.
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