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Author: search.filters.author.César BeltránSubject: search.filters.subject.Deep learning

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    A deep learning approach to distance map generation applied to automatic fiber diameter computation from digital micrographs
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-09-01)
    Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of (Formula presented.) and a mean square error (MSE) of (Formula presented.), demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing.
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    GeoNeXt: Efficient landslide mapping using a pre-trained ConvNeXt V2 encoder with a PSA-ASPP decoder
    (Elsevier BV, 2025-12-01)
    Landslides constitute one of the most destructive geological hazards worldwide, causing thousands of casualties and billions in economic losses annually. To mitigate these risks, accurate and efficient pixel-wise mapping of landslides for automatic semantic segmentation is of paramount importance. While recent advances in deep learning, particularly with transformer architectures and large pre-trained models like the Segment Anything Model (SAM), have shown promise, their application to landslide mapping is often hindered by high computational costs, prompt dependency, and challenges with data imbalance. To address these limitations, we propose GeoNeXt, a novel semantic segmentation architecture for intelligent landslide recognition. It combines a scalable, pre-trained ConvNeXt V2 encoder with a decoder that utilizes Pyramid Squeeze Attention (PSA) and Atrous Spatial Pyramid Pooling (ASPP) to capture multi-scale features. Through domain adaptation on the large-scale CAS landslide dataset, we refined the encoder's general pre-trained features to learn robust, landslide-specific features. GeoNeXt exhibited zero-shot transferability, achieving 74–78 % F1 and 64–66 % mIoU across three distinct test datasets from diverse regions, which were entirely excluded from the training process. Ablation studies on decoder variants validated the PSA-ASPP synergy, achieving a superior F1 of 90.39 % and mIoU of 83.18 % on the CAS dataset. Comparative analysis confirmed that GeoNeXt outperformed SAM-based methods, achieving F1 scores of 94.25 %, 86.43 %, and 92.27 % (mIoU: 89.51 %, 78.21 %, 86.02 %) on the Bijie, Landslide4Sense, and GVLM datasets, respectively, with 10× fewer parameters than SAM-based methods and lower computational demands. We showed that modernized convolutions, paired with strategic training, were a viable alternative to resource-intensive transformers. This efficiency facilitated their use in operational intelligent landslide recognition and geohazard monitoring systems.
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