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Simple Summary: The use of computer vision technology has improved the effectiveness of individual sheep identification, but existing methods face challenges such as large parameter sizes, slow recognition speeds, and difficult deployment. To address this issue, we have made improvements and optimizations based on the Retinaface face recognition model to create a balanced speed-accuracy sheep face recognition model. The optimized model has fewer parameters, simpler computations, faster inference speeds, and higher recognition accuracy, making it well-suited for deployment on resource-constrained edge devices. This research is expected to promote the application of deep learning-based sheep face recognition methods in production. The recognition of sheep faces based on computer vision has improved the efficiency and effectiveness of individual sheep identification, providing technical support for the development of smart farming. However, current recognition models have problems such as large parameter sizes, slow recognition speed, and difficult deployment. Therefore, this paper proposes an efficient and fast basic module called Eblock and uses it to build a lightweight sheep face recognition model called SheepFaceNet, which achieves the best balance between speed and accuracy. SheepFaceNet includes two modules: SheepFaceNetDet for detection and SheepFaceNetRec for recognition. SheepFaceNetDet uses Eblock to construct the backbone network to enhance feature extraction capability and efficiency, designs a bidirectional FPN layer (BiFPN) to enhance geometric location ability, and optimizes the network structure, which affects inference speed, to achieve fast and accurate sheep face detection. SheepFaceNetRec uses Eblock to construct the feature extraction network, uses ECA channel attention to improve the effectiveness of feature extraction, and uses multi-scale feature fusion to achieve fast and accurate sheep face recognition. On our self-built sheep face dataset, SheepFaceNet recognized 387 sheep face images per second with an accuracy rate of 97.75%, achieving an advanced balance between speed and accuracy. This research is expected to further promote the application of deep-learning-based sheep face recognition methods in production. [ABSTRACT FROM AUTHOR]

• Defect monitoring via convolutional neural networks (CNN). • Augmentation of experimental CNN training data with simulated data. • Finite element modeling of DMLS process. • Evaluation of the approach using standard CNN performance metrics. In the additive manufacturing (AM) industry, the direct metal laser solidification (DMLS) process has received significant research interest because of its ultra-high precision and geometry variability. However, many possible process errors can happen during the printing process and in-situ quality control is quite challenging because of the lack of appropriate monitoring and analysis capabilities. Thus, in this work, a part-scale finite element method (FEM) model is developed to investigate the heat transfer behavior of the DMLS process and to extract experimentally relevant thermal features. Specifically, a microscopic and a mesoscale sub-model are initially developed to describe powder properties and the laser behaviors, respectively, and their outputs are directly incorporated in the part-scale FEM model. The FEM model-generated data are then processed to replicate the long-wave infrared (LWIR) camera sensor outputs. Finally, both the experimental and FEM model-generated images are used to train machine learning algorithms for in-situ defect detection applications. In addition, we are endeavoring to look into a convolutional neural network utilizing such realistic thermal features from LWIR images with and without a variety of simulated data augmentation to examine its contribution to the CNN training. The thermal feature extraction and the machine learning algorithm are then utilized for the data reduction purpose. Also, a transmission strategy is demonstrated to filter out a significant amount of redundant data while maintaining high model prediction quality. [ABSTRACT FROM AUTHOR]

Recently, deep convolutional neural networks have achieved great success for medical image segmentation. However, unlike segmentation of natural images, most medical images such as MRI and CT are volumetric data. In order to make full use of volumetric information, 3D CNNs are widely used. However, 3D CNNs suffer from higher inference time and computation cost, which hinders their further clinical applications. Additionally, with the increased number of parameters, the risk of overfitting is higher, especially for medical images where data and annotations are expensive to acquire. To issue this problem, many 2.5D segmentation methods have been proposed to make use of volumetric spatial information with less computation cost. Despite these works lead to improvements on a variety of segmentation tasks, to the best of our knowledge, there has not previously been a large-scale empirical comparison of these methods. In this paper, we aim to present a review of the latest developments of 2.5D methods for volumetric medical image segmentation. Additionally, to compare the performance and effectiveness of these methods, we provide an empirical study of these methods on three representative segmentation tasks involving different modalities and targets. Our experimental results highlight that 3D CNNs may not always be the best choice. Despite all these 2.5D methods can bring performance gains to 2D baseline, not all the methods hold the benefits on different datasets. We hope the results and conclusions of our study will prove useful for the community on exploring and developing efficient volumetric medical image segmentation methods. • We summarize the latest developments about 2.5D segmentation methods for volumetric medical image segmentation tasks. • We make a large-scale empirical comparison of 2.5D segmentation methods with 2D and 3D methods on three representative public datasets involving different modalities (CT and MRI) and targets (cardiac, prostate and abdomen) to systematically evaluate the performance of these methods on different segmentation tasks. • We make conclusions base on the experimental results which we hope will prove useful for the community on exploring and developing efficient volumetric medical image segmentation methods. [ABSTRACT FROM AUTHOR]

Understanding the geological information of fractures from borehole images is essential for conducting a thorough geological analysis in engineering. Recently, the convolutional neural network (CNN) has been employed to extract high-level features of fractures from images. We propose an improved Faster R-CNN (region-based CNN) model for fracture detection, which is suitably equipped with a tailored region proposal network f -RPN for borehole fractures. The anchors generated by f -RPN are specifically designed with a fixed width and varying heights, and only the anchors with the maximum intersection-over-union values are labeled as positive. By taking advantage of the cyclic feature of borehole images, the images are expanded in four orientations to increase the amount and diversity of the model input. The detection results of the model can further be utilized to simplify the Hough transform for characterizing dip angles and dip directions of fractures. The proposed method is validated with borehole images from multiple projects in hydraulic engineering. Experiments reveal that the average detection accuracy is approximately 91.5%, which exhibits an improvement of 11.06% compared to the original Faster R-CNN. Further analysis indicates that both f -RPN and the multi-orientation expansion contribute to improving the prediction accuracy. It is also shown that this method demonstrates robustness against blurred image background and outperforms traditional image processing methods in the presence of noise. The fracture characterization by the simplified Hough transform turns out to be computationally efficient and accurate, with the absolute errors less than 5° compared to the manual identification results. • Faster R-CNN is improved with a tailored f -RPN for borehole fracture detection. • Borehole images are expanded in multiple orientations for data augmentation. • Simplified Hough transform based on Faster R-CNN results to characterize fractures. • The method is robust with blurred and noisy image data from real hydraulic engineering. [ABSTRACT FROM AUTHOR]