Why we need Laplacian for edge detection? I am studying some trivial computer vision processing techniques and I came across edge detection algorithms. IMO sharp changes in the gradient are enough indications to detect the edges in an image but most of the modern edge detection algorithms use Laplacian with second derivation to do the same task.

Q1. Are we using second derivative because the change in flux is a much better indicator for edges?
Q2. Can we use something other than Laplacian?
"Need" or "Want"?

Q1:  The second derivative avoids the problem of a gradually changing color being "greyish".

Q2:  Yes.

Here is one:

After advice about detecting focus quality of objects in a photo detected using YoloV3

It is based on how planar the color.  Very planar means no edge.

There are many, many other variations of these techniques possible.