Object Detection and Screen Presence Time Estimation Using Opencv and Yolo Alogrithm (International Journal of Food and Nutritional Sciences, 2022)
his paper describes about the object detection and object’s screen presence timeestimating model which will run on any computer easily. Currently there are only modelsthat will only detect the motion of the object alongside estimating screen time. But here wetried to propose a model that will detect the object that is present in the camera view andalso estimate the amount of time the object is present in the camera view. For objectdetection there are many algorithms such as CNN (Convolutional Neural Network), R-CNN,Fast R-CNN, Faster R-CNN, SSD (Single Shot Detector), YOLO (You Only Look Once) etc. Inthe current model we gave preference to the speed of detecting the object along with theaccuracy. So, we preferred using the Yolo algorithm among all the existing algorithms thatcan be used for the object detection. But yolo is only designed for using in the GPU basedcomputers. So, in order to implement yolo algorithm in our normal CPU based computerswe used OpenCV library such that real time object detection is also possible in Non-GPUcomputers. Our model estimates the time of screen presence of each object using pythonlibraries such as pandas, time etc.As we used yolo as our object detection algorithm our model detects objects with 80-99%confidence.