We investigate the methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy (uranium–molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on the F1 score. A F1 score of 95.1% was achieved for distinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions. We find that our newly developed microstructure representation describes image data well, and the traditional approach of utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relatively small, imbalanced original dataset of 272 images. To explore the applicability of generative methods for supplementing such limited datasets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image datasets are also discussed. Our work has implications for quantitative microstructure analysis and development of microstructure–processing relationships in limited datasets typical of metallurgical process design studies.
Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV-v2 , a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking of models for image classification, object detection, and 3D pose estimation. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods, which reveal that: 1) Some nuisance factors have a much stronger negative effect on the performance compared to others, also depending on the vision task. 2) Current approaches to enhance robustness have only marginal effects, and can even reduce robustness. 3) We do not observe significant differences between convolutional and transformer architectures. We believe our dataset provides a rich test bed to study robustness and will help push forward research in this area. Our dataset can be accessed from http://www.ood-cv.org/challenge.html.
With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.
Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification
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