Automated particle recognition for engine soot nanoparticles.

A pre-trained convolution neural network based on residual error functions (ResNet) was applied to the classification of soot and non-soot carbon nanoparticles in TEM images. Two depths of ResNet, one 18 layers deep and the other 50 layers deep, were trained using training-validation sets of increasing size (containing 100, 400 and 1400 images) and were assessed using an independent test set of 200 images. Network training was optimised in terms of mini-batch size, learning rate and training length.