Very high cycle fatigue life prediction of Ti60 alloy based on machine learning with data enhancement

Qian, Hongjiang; Huang, Zhiyong; Xu, Yeting; Zhou, Qikai; Wang, Jian; Shen, Jiebin; Shen, Zhenbin

doi:10.1016/j.engfracmech.2023.109431

Cited by 8 publications

(1 citation statement)

References 52 publications

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“…The image data in this study were obtained from the Precision Agriculture Experimental Base of the National Agricultural Informatization Engineering and Technology Research Centre in Xiaotangshan, Beijing, and the selected kale variety was Zhonggan-21, which was annotated according to the Pascal Voc dataset format using the open-source image annotation tool Labelme (v4.5.6), which was then preprocessed synchronously with the annotated images and their original images. In order to further expand the data samples, the randomly cropped images were subjected to data enhancement [17] operations, such as horizontal flipping, vertical flipping, brightness adjustment, and adding Gaussian noise. Finally, a dataset with a size of 5000 images was obtained.…”

Section: Experimental Datamentioning

confidence: 99%

A Study of Kale Recognition Based on Semantic Segmentation

Wu,

Guo,

Liu

et al. 2024

Agronomy

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The kale crop is an important bulk vegetable, and automatic segmentation to recognize kale is fundamental for effective field management. However, complex backgrounds and texture-rich edge details make fine segmentation of kale difficult. To this end, we constructed a kale dataset in a real field scenario and proposed an UperNet semantic segmentation model with a Swin transformer as the backbone network and improved the model according to the growth characteristics of kale. Firstly, a channel attention module (CAM) is introduced into the Swin transformer module to improve the representation ability of the network and enhance the extraction of kale outer leaf and leaf bulb information; secondly, the extraction accuracy of kale target edges is improved in the decoding part by designing an attention refinement module (ARM); lastly, the uneven distribution of classes is solved by modifying the optimizer and loss function to solve the class distribution problem. The experimental results show that the improved model in this paper has excellent performance in feature extraction, and the average intersection and merger ratio (mIOU) of the improved kale segmentation can be up to 91.2%, and the average pixel accuracy (mPA) can be up to 95.2%, which is 2.1 percentage points and 4.7 percentage points higher than the original UperNet model, respectively, and it effectively improves the segmentation recognition of kale.

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Section: Experimental Datamentioning

confidence: 99%