Which regularization in convolution layers (conv2D) I am using Keras for a project. I would like to know if it makes any sense to add any kind of regularization components such as kernel, bias or activity regularization in convolutional layers i.e Conv2D in Keras. 

If yes, then which regularization is most useful for conv2d layers


Kernel
Bias
Activity


As explained here the regularization techniques are useful for the fully connected(dense) layers. Any such intuition/logic for conv2D?
Nowadays, people don't tend to add much regularisation like $L_2$ to convolutional networks. Usually, regularisation is achieved through the use of techniques like dropout and batch normalisation (although exactly how this regularisation actually happens is not well understood). 

Having said that, it may be different for your particular problem. I recommend that after experimenting with dropout and batch norm, you should experiment with $L_2$ and see how the predictive performance is affected on a validation set.