Can you remember the title of the paper?

I mean it's a very good project to develop but a visual inspection can solve it? Unless you perform an automation process of generating camo for something to be placed in the wild like a box for a camera or something.

But in fact is a very good ML project

You can try to experiment with different techniques and libraries until you find an interesting solution maybe in that part you need to code something new.

But focus on what's the product of your research, with low inspection form advisor as in my case, it is very easy to start flying and working in many and none things at the time

I'm in ML research too.

As some suggested don't reinvent the wheel if it is something already done, as academic researcher you will never compete with industrial researchers. They have tons of programmers focusing on optimizing algorithms and libraries.

But if your research is a suggestion of new equations or schemas you should probably try in order to publish it, but if you will use a technique in some part of the work or it's an application of technique in a forgotten area usea what works and add something than differentiates your work.

For example I'm working in time series forecasting, my suggestion so far is to use MODWT + stochastic + GRU instead of EEMD + LSTM, who knows if it will work I will not implement DWT or GRU from scratch

Haven't used all of them but I suggest that instead of focusing on the models focus on the data, review what transformation or cleaning on data could provide better results. Decomposition, decorrelation, log, wavelets etc

If the models use the same algorithm probably results won't change at all. For example Wavelet Toolbox on Matlab and PyWavelets provide same results even though are different teams of developers, but they use the same DWT algorithm.

I love wavelets