Semantic segmentation for unmanned aerial vehicle (UAV) remote sensing images has become one of the research focuses in the field of remote sensing at present, which could accurately analyze the ground objects and their relationships. However, conventional semantic segmentation methods based on deep learning require large-scale models that are not suitable for resource-constrained UAV remote sensing tasks. Therefore, it is important to construct a light-weight semantic segmentation method for UAV remote sensing images. With this motivation, we propose a light-weight neural network model with fewer parameters to solve the problem of semantic segmentation of UAV remote sensing images. The network adopts an encoder-decoder architecture. In the encoder, we build a light-weight convolutional neural network model with fewer channels of each layers to reduce the number of model parameters. Then, feature maps of different scales from the encoder are concatenated together after resizing to carry out the multi-scale fusion. Moreover, we employ two attention modules to capture the global semantic information from the context and the correlation among channels in UAV remote sensing images. In the decoder part, the model obtains predictions of each pixel through the softmax function.We conducted experiments on the ISPRS Vaihingen dataset, UAVid dataset and UDD6 dataset to verify the effectiveness of the light-weight network. Our method obtains quality semantic segmentation results evaluated on UAV remote sensing datasets with only 9M parameters the model owns, which is competitive among popular methods with the same level of parameters.

Objective To address the relationship between apolipoprotein E (ApoE) and oxidative stress in gestational diabetes mellitus (GDM). Methods Fifty pregnant women diagnosed with GDM and normal pregnant women were recruited separately and their blood and placental tissue were collected. Western blot assay, qRT‐PCR assay and ELISA were used to detect the expression levels of ApoE and other oxidative stress factors in these samples. ApoE−/− mice with a C57BL/6J background were used to evaluate the relationship between ApoE deficiency and oxidative stress during GDM. Results Serum and placental ApoE were both down‐regulated in GDM patients (serum: 45.25 ± 19.27 μg/ml for GDM and 96.34 ± 24.05 μg/ml for control; placental: 14.49 ± 6.52 ng/mg tissue for GDM and 48.76 ± 13.59 ng/mg tissue for control). There was a statistical correlation between placental ApoE level and oxidative stress in GDM (r = −0.4904 with MDA, −0.4258 with NO, 0.4476 with SOD, 0.6316 with GSH). ApoE deficiency exacerbated blood glucose, insulin anomaly and oxidative stress in placenta in GDM mouse models. Placental Apo E deficiency correlates to oxidative stress in GDM. Conclusion In conclusion, we innovatively revealed the relationship between ApoE and GDM oxidative stress among GDM patients in this study.

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