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. In all tests, ResNet18 and ResNet50 had statistically similar performances, though ResNet18 required only 25–35% of the training time of ResNet50. Training using the 100‐, 400‐ and 1400‐image training‐validation sets led to classification accuracies of 84%, 88% and 95%, respectively. ResNet18 and ResNet50 were also compared for their ability to categorise soot and non‐soot nanoparticles via a fivefold cross‐validation experiment using the entire set of 800 images of soot and 800 images of non‐soot. Cross‐validation was repeated 3 times with different training durations. For all cross‐validation experiments, classification accuracy exceeded 91%, with no statistical differences between any of the network trainings. The most efficient network was ResNet18 trained for 5 epochs, which reached 91.2% classification after only 84 s of training on 1600 images. Use of ResNet for classification of 1000 images, the amount suggested for reliable characterisation of soot sample, requires <4 s, compared with >30 min for a skilled operator classifying images manually. Use of convolution neural networks for classification of soot and non‐soot nanoparticles in TEM images is highly promising, particularly when manually classified data sets have already been established.