Adrian Rosebrock, a known CV researcher, states in his "Gentle guide to deep learning object detection" that: "object detection, regardless of whether performed via deep learning or other computer vision techniques, builds on image classification and seeks to localize precisely an area where an object appears". One approach to build a custom object detector, as he suggests, is to choose any classifier and precede it with an algorithm to select and provide regions of an image that may contain an object. Within this method, you are free to decide whether to use a traditional ML algorithm for image classification (utilising or not CNN as a feature extractor) or train a simple neural network to handle arbitrary large datasets. Despite its proven efficiency, this two-stage object detection paradigm, known as R-CNN, still relies on heavy computations and is not suitable for real-time application. It is further said in the abovementioned post that "another approach is to treat a pre-trained classification network as a base (backbone) network in a multi-component deep learning object detection framework (such as Faster R-CNN, SSD, or YOLO)".

The data and targets are then split into training, and testing data be keeping 10% of data as testing and 90% as training data. A checkpoint is created, which will save the model, which will have the minimum validation loss. Then the training data is then fitted in the model so that predictions can be made in the future.

Today's blog post was inspired by PyImageSearch reader, Emmanuel. Emmanuel emailed me after last week's tutorial on object detection with deep learning OpenCV and asked: I really enjoyed last week's blog post on object detection with deep learning and OpenCV, thanks for putting it together and for making deep learning with OpenCV so accessible. I want to apply the same technique to real-time video. What is the best way to do this? How can I achieve the most efficiency?

Today's blog post was inspired by PyImageSearch reader, Emmanuel. Emmanuel emailed me after last week's tutorial on object detection with deep learning OpenCV and asked: I really enjoyed last week's blog post on object detection with deep learning and OpenCV, thanks for putting it together and for making deep learning with OpenCV so accessible. I want to apply the same technique to real-time video. What is the best way to do this? How can I achieve the most efficiency?

A couple weeks ago we learned how to classify images using deep learning and OpenCV 3.3's deep neural network ( dnn) module. While this original blog post demonstrated how we can categorize an image into one of ImageNet's 1,000 separate class labels it could not tell us where an object resides in image. In order to obtain the bounding box (x, y)-coordinates for an object in a image we need to instead apply object detection. Object detection can not only tell us what is in an image but also where the object is as well. In the remainder of today's blog post we'll discuss how to apply object detection using deep learning and OpenCV.