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What if instead of trained detection you use template matching?

Since it's static, and has very low variance, the image should look wery similar, and the position should be consistent too, so you dont't need to use the full image for matching, which can be heavy on resources, you only need an estimated ROI as a subMat. Use the image of the boss as input, and given the low variance, there should be a close match (significantly lover than other bosses) at the position of the boss.

After the template match, you look at the minimum value, and decide a minimal threshold, below you accept it as detected.

It fullifies your objective, but has zero inpact on your knowledge about Cascade Classifiers, nor answers your questions.