YOLO – real-time object detection

To learn how to use deep learning in OpenCV, we are going to present an example of object detection and classification based on the YOLO algorithm. This is one of the fastest object detection and recognition algorithms, which can run at around 30 fps in an NVIDIA Titan X.

YOLO v3 deep learning model architecture

Common object detection in classical computer vision uses a sliding window to detect objects, scanning a whole image with different window sizes and scales. The main problem here is the huge time consumption in scanning the image several times to find objects.

YOLO uses a different approach by dividing the diagram into an S x S grid. For each grid, YOLO checks for B bounding boxes, and then the deep learning model extracts the bounding boxes for each patch,the confidence to contain a possible object, and the confidence of each category in the training dataset per each box. The following screenshot shows the S x S grid:

YOLO is trained with a grid of...