There is a high demand for fully automated methods for the analysis of particle size distributions of agglomerated, sintered or occluded primary particles. Therefore, a novel, deep learning-based, method for the pixel-perfect detection and sizing of agglomerated, aggregated or occluded primary particles was proposed and tested.As a specialty, the training of the utilized convolutional neural networks was carried out using only synthetic images, to avoid the laborious task of manual annotation and to increase the quality of the ground truth. Despite the training on synthetic images, the proposed method performs excellent on real world samples of sintered silica nanoparticles with various sintering degrees and varying image conditions.In a direct comparison, the proposed method clearly outperforms two state-of-the-art methods for automated image-based particle size analysis (Hough transformation and the ImageJ ParticleSizer plug-in), with respect to precision and speed, thereby advancing into regions of human-like performance and reliability.

Powders and their mixtures are elemental for many industries (e.g., food, pharmaceutical, mining, agricultural, and chemical). The properties of the manufactured products are often directly linked to the particle properties (e.g., particle size and shape distribution) of the utilized powder mixtures. The most straightforward approach to acquire information concerning these particle properties is image capturing. However, the analysis of the resulting images often requires manual labor and is therefore time-consuming and costly. Therefore, the work at hand evaluates the suitability of Mask R-CNN—one of the best-known deep learning architectures for object detection—for the fully automated image-based analysis of particle mixtures, by comparing it to a conventional, i.e., not machine learning-based, image analysis method, as well as the results of a trifold manual analysis. To avoid the need of a laborious manual annotation, the training data required by Mask R-CNN are produced via image synthesis. As an example for an industrially relevant particle mixture, endoscopic images from a fluid catalytic cracking reactor are used as a test case for the evaluation of the tested methods. According to the results of the evaluation, Mask R-CNN is a well-suited method for the fully automatic image-based analysis of particle mixtures. It allows for the detection and classification of particles with an accuracy of 42.7% for the utilized data, as well as the characterization of the particle shape. Also, it enables the measurement of the mixture component particle size distributions with errors (relative to the manual reference) as low as −2±5 for the geometric mean diameter and −6±5% for the geometric standard deviation of the dark particle class of the utilized data, as well as −8±4% for the geometric mean diameter and −6±2% for the geometric standard deviation of the light particle class of the utilized data. Source code, as well as training, validation, and test data publicly available.

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There is a high demand for fully automated methods for the analysis of primary particle size distributions of agglomerates on transmission electron microscopy images. Therefore, a novel method, based on the utilization of artificial neural networks, was proposed, implemented and validated.The training of the artificial neural networks requires large quantities (up to several hundreds of thousands) of transmission electron microscopy images of agglomerates consisting of primary particles with known sizes. Since the manual evaluation of such large amounts of transmission electron microscopy images is not feasible, a synthesis of lifelike transmission electron microscopy images as training data was implemented.The proposed method can compete with state-of-the-art automated imaging particle size methods like the Hough transformation, ultimate erosion and watershed transformation and is in some cases even able to outperform these methods. It is however still outperformed by the manual analysis.

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