☆ 4.7 Article

Weighted skip-connection feature fusion: A method for augmenting UAV oriented rice panicle image segmentation

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2023)

An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images

Abolfazl Abdollahi et al.

Summary: Extracting building features from high-resolution aerial imagery is challenging due to obstacles such as shadows, trees, and cars. This study proposes a new deep learning method, Seg-Unet, which combines Segnet and Unet techniques to improve the accuracy of building extraction from high-resolution remote sensing images. The proposed method outperforms other methods and achieves 92.73% accuracy on the Massachusetts building dataset.

GEOCARTO INTERNATIONAL (2022)

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Article Engineering, Multidisciplinary

A research on an improved Unet-based concrete crack detection algorithm

Lingxin Zhang et al.

Summary: The paper proposed the CrackUnet model based on deep learning, which uses resized, labeled, and augmented crack images to create a dataset, and adopts a new loss function called generalized Dice loss to improve crack detection accuracy. The study investigates the impact of dataset size and model depth on training time, detection accuracy, and speed, showing that the CrackUnet model outperforms other methods with strong robustness and generalization.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

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Proceedings Paper Computer Science, Artificial Intelligence