Description

Multiple Object recognition using Yolo v3 Deep learning

In this Object recognition technique, Yolo v3 Model is used, which is the pre-trained model trained to recognize multiple objects.Early Deep Learning based object detection algorithms like the R-CNN and Fast R-CNN used a method to narrow down the number of bounding boxes that the algorithm had to test.This was followed by Faster R-CNN that used a Region Proposal Network (RPN) for identifying bounding boxes that needed to be tested. By clever design the features extracted for recognizing objects, were also used by the RPN for proposing potential bounding boxes thus saving a lot of computation.

Deep learning has gained a tremendous influence on how the world is adapting to Artificial Intelligence since past few years. Some of the popular object detection algorithms are Region-based Convolutional Neural Networks (RCNN), Faster-RCNN, Single Shot Detector (SSD) and You Only Look Once (YOLO). Amongst these, Faster-RCNN and SSD have better accuracy, while YOLO performs better when speed is given preference over accuracy.