Incorporating Mask R-CNN and YOLOv5 for Improved Object Detection

Humans can easily identify multiple objects in an image or video but for computers it is very difficult to identify. In the previous decades scientists think that a computer cannot detect an object in the images or videos. Now there is a success in detecting an object by the computer. Detecting and precisely identifying objects swiftly presents a highly formidable undertaking, but now a days we can do it with maximum accuracy with the help of different object detection techniques. Object detection is the task of detecting instances of objects of a certain class within an image. Object detection techniques train predictive models or use template matching to locate and classify objects and process these objects in the same way as humans do and then provide an appropriate output. Object detection algorithms typically use machine learning or deep learning techniques to produce meaningful results. Object detection is a technology includes computer vision and image processing used to detect the objects in given videos. This work describes about detecting of an object by embedding of two techniques Mask- RCNN and YOLO-v5.