In this tutorial, you will learn how to apply Holistically-Nested Edge Detection (HED) with OpenCV and Deep Learning. We'll apply Holistically-Nested Edge Detection to both images and video streams, followed by comparing the results to OpenCV's standard Canny edge detector. Edge detection enables us to find the boundaries of objects in images and was one of the first applied use cases of image processing and computer vision. When it comes to edge detection with OpenCV you'll most likely utilize the Canny edge detector; however, there are a few problems with the Canny edge detector, namely: Holistically-Nested Edge Detection (HED) attempts to address the limitations of the Canny edge detector through an end-to-end deep neural network. This network accepts an RGB image as an input and then produces an edge map as an output.