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1. An Improved DeeplabV3+ Tile Image Positioning Method NSTL国家科技图书文献中心

Xiaoyang Li |  Rui Wang... -  《The International Conference Optoelectronic Information and Optical Engineering (OIOE2024),Part One of Two Parts》 -  International Conference Optoelectronic Information and Optical Engineering - 2025, - 135132B.1～135132B.8 - 共8页

摘要： model based on DeepLabV3+and combined with MobileNetv2 | In order to improve the detection accuracy and |  efficiency of surface defects on ceramic tiles, a detection |  for feature extraction was constructed. Using ECA |  attention mechanism and multi branch receptive field
关键词： Image segmentation |  DeeplabV3+ |  ECA attention mechanism |  MobileNetV2 |  E-ASPP
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2. Improved DeepLabV3+ model for cloud detection in remote sensing images NSTL国家科技图书文献中心

Ze Chen |  Guoman Huang... -  《Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024),Part Two of Two Parts》 -  International Conference on Geoscience and Remote Sensing Mapping - 2025, - 135062G.1～135062G.8 - 共8页

摘要： on improved DeeplabV3+, which improves the accuracy |  than that of the traditional DeeplabV3+. In addition | The importance of optical remote sensing |  images in Earth observation is increasing, but the |  presence of clouds significantly affects the clarity and
关键词： Cloud detection |  DeeplabV3+ |  Coordinate attention mechanism |  DenseASPP
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3. Lightweight insulator defect detection algorithm based on improved Deeplabv3+ and YOLOv5s NSTL国家科技图书文献中心

Hongwei Hu |  Wenxuan Peng... -  《International Conference on Computer Vision and Image Processing (CVIP 2024)》 -  International Conference on Computer Vision and Image Processing - 2025, - 135210N.1～135210N.7 - 共7页

摘要： DeepLabv3+ model which is trained based on ADAM optimizer | Intelligent recognition of insulator infrared |  image defects based on UAV inspection has important |  research value in transmission line inspection. This |  paper proposes a lightweight deep neural network that
关键词： Lightweight |  Deeplabv3+ |  YOLOv5s |  GhostNet |  VanillaNet
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4. Semantic segmentation of landslide image using DeepLabv3+ and completed local binary pattern NSTL国家科技图书文献中心

Wei Wang |  Zhihua Zhang... -  《Journal of Applied Remote Sensing》 - 2025,19(1) - 14502～14502 - 共22页

摘要：. We propose a fusion DeepLabv3+ and completed local |  segmentation method (CLBP-DeepLabv3+), using the improved |  constitutes the CLBP-DeepLabv3+ model. Through ablation |  of 80.53%. Compared with the original DeepLabv3 | + model, the improved DeepLabv3+ increased the mIoU by
关键词： Semantic segmentation |  DeepLabv3+ |  Landslide |  Remote sensing image |  Completed local binary pattern |  Feature aggregation module
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5. Automatic semantic segmentation of breast cancer in DCE-MRI using DeepLabV3+ with modified ResNet50 NSTL国家科技图书文献中心

Sahaya Pushpa Sarmil... |  Milton A.... -  《Biomedical signal processing and control》 - 2025,99(Jan.) - 1.1～1.12 - 共12页

摘要： as the backbone for the DeepLabV3+ network. The |  DeepLabV3+ with RN50D or PLA-RN50D is a powerful and |  superior segmentation performance of DeepLabV3+ with PLA | © 2024 Elsevier LtdResearch on breast cancer |  segmentation is essential due to its high prevalence as the
关键词： Convolutional Neural Network |  DeepLabV3+ |  Dynamic Contrast Enhanced Magnetic Resonance Imaging |  Semantic Segmentation
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6. The DeepLabV3+ Algorithm Combined With the ResNeXt Network for Medical Image Segmentation NSTL国家科技图书文献中心

YanyanWu |  Yajun Xie... -  《Concurrency and computation: practice and experience》 - 2025,37(4/5) - e8386.1～e8386.18 - 共18页

摘要： algorithm for medical images, leveraging the DeepLabV3 | This paper presents a semantic segmentation | + architecture in conjunction with the ResNeXt network. The |  proposed algorithm takes into account the correlation |  between each structure of lung images and the unique
关键词： DeepLabV3+ |  dense pyramidal pooling |  ResNeXt |  semantic segmentation
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7. Accurate cotton verticillium wilt segmentation in field background based on the two-stage lightweight DeepLabV3+model NSTL国家科技图书文献中心

Xu, Ying |  Ma, Benxue... -  《Computers and Electronics in Agriculture》 - 2025,229 - 共14页

摘要： based on improved DeepLabV3+ was developed, which can | , respectively. Compared to the original DeepLabV3+ model, the | Verticillium wilt is a common disease that |  severely affects cotton yields. To address the challenges |  of low efficiency for detecting the cotton
关键词： Cotton verticillium wilt |  Two-stage semantic segmentation |  DeepLabV3+ |  Channel pruning |  Knowledge distillation
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8. Segmentation of tunnel water leakage based on a lightweight DeepLabV3+model NSTL国家科技图书文献中心

Wang, Dandan |  Hou, Gongyu... -  《Measurement Science & Technology》 - 2025,36(1) - 1～16 - 共16页

摘要： Deeplabv3+ model and adopts MobileNetv3-Large as the | The accurate and efficient detection of water |  leakage with complex backgrounds is crucial for the |  safety of metro operations. A lightweight segmentation |  method for metro tunnel water leakage based on transfer
关键词： tunnel water leakage |  semantic segmentation |  Deeplabv3+ |  attention mechanism |  transfer learning
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9. A predictive model for the freeze-thaw concrete durability index utilizing the deeplabv3+ model with machine learning NSTL国家科技图书文献中心

Daming Luo |  Xudong Qiao... -  《Construction and Building Materials》 - 2025,459(Jan.17) - 139788.1～139788.15 - 共15页

摘要： segmentation model for concrete by improving the Deeplabv3 |  that the improved Deeplabv3 + network model | When quantifying the damage inflicted on |  concrete by freeze-thaw cycles, traditional physical | -mathematical models are characterized by an excessive number
关键词： Concrete durability |  Freeze-thaw cycles |  Performance prediction |  Deeplabv3+ |  Machine learning |  Artificial intelligence
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10. DeepLabv3 + method for detecting and segmenting apical lesions on panoramic radiography NSTL国家科技图书文献中心

Fatmanur Ketenci Çay |  Çağrı Yeşil... -  《Clinical oral investigations》 - 2025,29(2) - Article: 101～Article: 101 - 共10页

摘要： DeepLabv3 + model and compare it with the U-Net model in |  reviewer. The DeepLabv3 + model, one of the state-of-the |  the literature. Results: The DeepLabv3 + and U-Net |  DeepLabv3 + were 29.96% and 61.06% better than the U-Net |  DeepLabv3 + model. The difference in the IoU results of
关键词： Apical lesions; Artificial intelligence; Deep learning; Semantic segmentation.
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