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Bilateral U-Net semantic segmentation with spatial attention mechanism

CAAI Transactions on Intelligence Technology 2023年第2期8卷 297-307页

作者： Guangzhe Zhao Yimeng Zhang Maoning Ge Min Yu Beijing University of Civil Engineering and Architecture College of Electrical and Information EngineeringBeijingChina Graduate School of Informatics Nagoya UniversityNagoyaJapan Department of General Surgery Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina

Aiming at the problem that the existing models have a poor segmentation effect on imbalanced data sets with small-scale samples,a bilateral U-Net network model with a spatial attention mechanism is designed.The model uses the lightweight MobileNetV2 as the backbone network for feature hierarchical extraction and proposes an Attentive Pyramid spatial attention(APSA)module compared to the Attenuated spatial Pyramid module,which can increase the receptive field and enhance the information,and finally adds the context fusion prediction branch that fuses high-semantic and low-semantic prediction results,and the model effectively improves the segmentation accuracy of small data sets.The experimental results on the CamVid data set show that compared with some existing semantic segmentation networks,the algorithm has a better segmentation effect and segmentation accuracy,and its mIOU reaches 75.85%.Moreover,to verify the generality of the model and the effectiveness of the APSA module,experiments were conducted on the VOC 2012 data set,and the APSA module improved mIOU by about 12.2%.

关键词： attention mechanism receptive field semantic fusion semantic segmentation spatial attention module U-Net

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An attention-based prototypical network for forest fire smoke few-shot detection

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Journal of Forestry Research 2022年第5期33卷 1493-1504页

作者： Tingting Li Haowei Zhu Chunhe Hu Junguo Zhang School of Technology Beijing Forestry UniversityBeijing 100083People’s Republic of China Key Lab of State Forestry and Grassland Administration for Forestry Equipment and Automation Beijing 100083People’s Republic of China

Existing almost deep learning methods rely on a large amount of annotated data, so they are inappropriate for forest fire smoke detection with limited data. In this paper, a novel hybrid attention-based few-shot learning method, named attention-Based Prototypical Network, is proposed for forest fire smoke detection. Specifically, feature extraction network, which consists of convolutional block attention module, could extract high-level and discriminative features and further decrease the false alarm rate resulting from suspected smoke areas. Moreover, we design a metalearning module to alleviate the overfitting issue caused by limited smoke images, and the meta-learning network enables achieving effective detection via comparing the distance between the class prototype of support images and the features of query images. A series of experiments on forest fire smoke datasets and miniImageNet dataset testify that the proposed method is superior to state-of-the-art few-shot learning approaches.

关键词： Forest fire smoke detection Few-shot learning Channel attention module spatial attention module Prototypical network

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