A gentle guide to deep learning object detection - PyImageSearch

Today's blog post is inspired by PyImageSearch reader Ezekiel, who emailed me last week and asked: I went through your previous blog post on deep learning object detection along with the followup tutorial for real-time deep learning object detection. I've been using your source code in my example projects but I'm having two issues: I would really appreciate it if you could cover this in a blog post. In fact, if you go through the comments section of my two most recent posts on deep learning object detection (linked above), you'll find that one of the most common questions is typically (paraphrased): How do I modify your source code to include my own object classes? Since this appears to be such a common question, and ultimately a misunderstanding on how neural networks/deep learning object detectors actually work, I decided to revisit the topic of deep learning object detection in today's blog post. To learn more about deep learning object detections, and perhaps even debunk a few misconceptions or misunderstandings you may have with deep learning-based object detection, just keep reading. Today's blog post is meant to be a gentle introduction to deep learning-based object detection. I've done my best to provide a review of the components of deep learning object detectors, including OpenCV Python source code to perform deep learning using a pre-trained object detector.