###### Abstract  
Diamond grinding wheels (DGWs) have a central role in cutting-edge industries such as aeronautics or defense and spatial applications. Characterizations of DGWs are essential to optimize the design and machining performance of such cutting tools. Thus, the critical issue of DGW characterization lies in the detection of diamond grits. However, the traditional diamond detection methods rely on manual operations on DGW images. These methods are time-consuming, error-prone and inaccurate. In addition, the manual detection of diamond grits remains challenging even for a subject expert. To overcome these shortcomings, we introduce a deep learning approach for automatic diamond grit segmentation. Due to our small dataset of 153 images, the proposed approach leverages transfer learning techniques with pre-trained ResNet34 as an encoder of U-Net CNN architecture. Moreover, with more than 8600 hyperparameter combinations in our model, manually finding the best configuration is impossible. That is why we use a Bayesian optimization algorithm using Hyperband early stopping mechanisms to automatically explore the search space and find the best hyperparameter values. Moreover, considering our small dataset, we obtain overall satisfactory performance with over 53% IoU and 69% F1-score. Finally, this work provides a first step toward diamond grinding wheel characterization by using a data-driven approach for automatic semantic segmentation of diamond grits. deep learning; Bayesian hyperparameter optimization; computer vision; semantic segmentation; U-Net; diamond abrasive grits; diamond grinding wheels Article  
Deep Learning and Bayesian Hyperparameter Optimization: A Data-Driven Approach for Diamond Grit Segmentation  
1 Laboratoire Interdisciplinaire Carnot de Bourgogne, PMDM, ICB-UMR6303, CNRS, Universite de Bourgogne Franche-Comte, 9 Avenue Alain Savary, BP47870, CEDEX, 21078 Dijon, France 12 FEMTO-STI UMR 6174, CNRS, Universite de Bourgogne Franche-Comte, UTRM, F-90010 Belfort, France 2  
3 DIAMATEC, Route de Grachaux, 70700 Olsalev-et-Grachaux, France 3  
[PERSON]  
1 Laboratoire Interdisciplinaire Carnot de Bourgogne, PMDM, ICB-UMR6303, CNRS, Universite de Bourgogne Franche-Comte, 9 Avenue Alain Savary, BP47870, CEDEX, 21078 Dijon, France 1  
2 FEMTO-STI UMR 6174, CNRS, Universite de Bourgogne Franche-Comte, UTRM, F-90010 Belfort, France 2  
Dipartimento di Matematica, Universita di B As the abrasive power evaluation systems are difficult to set up and use in an industrial environment, more promising approaches are computer vision-based methods. The aim of such methods is to leverage the knowledge of subject experts in order to characterize DGWs from images. In these approaches, one of the major challenges is the detection of diamond grits and their interfaces. Generally, diamond detection methods rely on manual operations on DGW images. However, these methods are time-consuming, error-prone and inaccurate. In addition, due to the diversity of DGW image acquisition parameters, it is challenging to generalize traditional image processing algorithms for an accurate semantic segmentation of diamond grits. In recent years, in the data-driven era, with the rise of deep learning algorithms, semantic segmentation has become an extensively studied research area in the field of computer vision [7, 8]. The aim of semantic segmentation algorithms [9] is to assign a separate class label to each pixel of an image for object detection purposes. Applications of such algorithms can be found in autonomous vehicles [10], where the pixel labels could be trees, cars, humans or roads, and in medical image analysis [11, 12], where they could be cells, tumors or even brain activity analysis [13, 14]. For DGW image segmentation, it is about diamond abrasive grits. The semantic understanding of a scene by a computer is a challenging problem mainly solved today thanks to the development of convolutional neural networks (CNNs) by [PERSON] et al. [15] and the computational resources of modern computers.