Once the Mask R-CNN is running the next necessary step is to optimize it with the objective of being able to perform activities in real-time conditions. For this purpose, we thought about two possible solutions.
The first one was to use GPU support, which could reduce the time of execution considerably. I tried to use the GPU that Google provides for free in Google Colab (https://colab.research.google.com/) but I’ve been having some problems to install the necessary dependencies of the project so I will try it in the GPU available at the JdeRobot lab.
The second improvement could be given with the incorporation of a feature-tracking algorithm in the application, this one has not been implemented yet. Furthermore I measured the execution times of the application with different input images. I watched the performance with one or more objects and the performance with a bigger area to be segmented or a smaller one. The conclusion is that the influence of this parameters is not really important in the final execution time. After that I tried with smaller input images but the network model seems to have problems with some image sizes.