Deep Learning based Object Detection using YOLOv3 with OpenCV ( Python / C++ )

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Deep Learning based Object Detection using YOLOv3 with OpenCV ( Python / C++ ) (learnopencv.com)

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https://youtu.be/WP4F6aK1Ft8

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In this post, we will learn how to use YOLOv3 — a state of the art object detector — with OpenCV.

YOLOv3 is the latest variant of a popular object detection algorithm YOLO – You Only Look Once. The published model recognizes 80 different objects in images and videos, but most importantly it is super fast and nearly as accurate as Single Shot MultiBox (SSD).

Starting with OpenCV 3.4.2, you can easily use YOLOv3 models in your own OpenCV application.

This post mainly focusses on inference, but if you want to train your own YOLOv3 model on your dataset, you will find our tutorial for the same in this follow-up post.

How does YOLO work ?

We can think of an object detector as a combination of a object locator and an object recognizer.

In traditional computer vision approaches, a sliding window was used to look for objects at different locations and scales. Because this was such an expensive operation, the aspect ratio of the object was usually assumed to be fixed.

Early Deep Learning based object detection algorithms like the R-CNN and Fast R-CNN used a method called Selective Search to narrow down the number of bounding boxes that the algorithm had to test.

Another approach called Overfeat involved scanning the image at multiple scales using sliding windows-like mechanisms done convolutionally.

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.

YOLO on the other hand approaches the object detection problem in a completely different way. It forwards the whole image only once through the network. SSD is another object detection algorithm that forwards the image once though a deep learning network, but YOLOv3 is much faster than SSD while achieving very comparable accuracy. YOLOv3 gives faster than realtime results on a M40, TitanX or 1080 Ti GPUs.

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