Fig. 1: Image preprocessing pipeline and the model architecture. | npj Computational Materials

Fig. 1: Image preprocessing pipeline and the model architecture.

From: Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data

Fig. 1: Image preprocessing pipeline and the model architecture.The alternative text for this image may have been generated using AI.

a The geometry of the measurements. After Lorentz-polarization correction, measured diffraction patterns are sequentially converted from detector coordinates (a) to reciprocal space (b) and then to polar coordinates (c). For the detection, the contrast is enhanced by CLAHE (all shown images are already contrast-enhanced for visualization). d Feature extractor with asymmetric feature maps; feature shapes correspond to an input image size of 512 × 512 pixels. e The Region Proposal Network kernels convolve with feature maps to extract RoI at different scales. f At the second detection stage, a RoIAlign layer extracts features at corresponding positions from the largest feature map, from which the box coordinates and the score of confidence are predicted for each box by a fully-connected network.

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