With the advancements in technology made in the 21st century, object detection is a topic that has attracted a lot of attention in recent years. It is probably the most well known term within the domain of computer vision and it encounters some really interesting problems. To gain a complete image understanding, we should not only concentrate on classifying different images but also try to precisely estimate the concepts and locations of objects contained in each image. This task is referred to as object detection. Object recognition is a collection of related tasks for identifying objects in digital photographs. Here, to perform object detection of different safety equipment, we have utilized a predefined framework/pretrained model as they are convenient for serving our purpose. The aforementioned framework we have used for object detection is – “YOLO” (You Only Look Once). It’s a supremely fast and accurate framework that can process at 45 frames per second (in real-time) and also understands generalized object representation. We have initially given an image as input to YOLO and the framework then divides the input image into grids. Image classification and localization are applied on each grid. We have created a website for the deployment of our model using “Flask”. It is a web application framework to compile modules and libraries which will also help the developer to write web applications without writing low-level code like thread management and protocols. We have also used Heroku as a platform to deploy and manage our web application. Heroku is a container-based cloud Platform as a Service (PaaS) that can be used to instantly extend applications with fully managed services. After the model deployment is done, we can ask for the users to upload images of their choice through the website. YOLO then predicts the bounding boxes and their corresponding class probabilities for objects (if any are found). The classes we have categorized the images into are listed