How to measure a detector's performance under extremely complex real-world environment?
on an ROC curve your plotting FP(x-axis) vs TP(y-axis) They are calculated by:  

True-positives = # of correctly detected positives/# of actual positives 

False-Positives = # of positives that are not true-positives/ number of positives 

Now on your ROC curve, because you did not mention incuding this into your question above, you most likely did not include the "coin-flip" line. That is the line y = x (from x = 0-1)

This line represents how well your algorithm performs by just guessing randomly (Yes, No) 

If your algorithm is a decent detector, the ROC curve will it will be above the coin-flip line, if it is below this line it is better at being an anti-detector, i.e., when there is no event and would be a poor detector for your application. 

I've worked extensively on this for my master's thesis on onset detection. Below is an example of ROC curves plotted against the coin-flip line.