基于对抗生长的目标检测方法

Shijie, 邓世杰 Deng; Haiyan, 王海晏 Wang; An, 徐安 Xu; Chunqing, 高春庆 Gao; Junbing, 李俊兵 Li

doi:10.3788/aos202242.0210002

光学学报

2022

DOI: 10.3788/aos202242.0210002

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基于对抗生长的目标检测方法

邓世杰 Deng Shijie¹,

王海晏 Wang Haiyan²,

徐安 Xu An³

et al.

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2023

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基于隐私保护机制的辐射光源衍射图像筛选

Xu Kang,

Zhu Yongxin,

Wu Bo

et al. 2023

激光与光电子学进展

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基于隐私保护机制的辐射光源衍射图像筛选

Xu Kang,

Zhu Yongxin,

Wu Bo

et al. 2023

激光与光电子学进展

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基于光场统计的机场飞鸟目标检测

Liu Guochun,

Huang Sheng,

Wei Guiming

et al. 2023

Laser Optoelectron. Prog.

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Airport bird detection requires a precise and reliable detection method for small and weakly moving targets because bird strikes are the primary accident sources affecting airport safety. The airport bird detection was realized under a wide field of view and against a complex background after completing experiments under various lighting conditions. The light field fluctuations were used to enhance the low signaltonoise ratio of the bird motion characteristic signal, and the local Gaussian mixture model was used to segment the foreground of the image enhancement area. The experimental findings demonstrate the effectiveness of the proposed algorithm in increasing the detection rate of dim small targets with long distances, a wide field of view, and low signaltonoise ratios while having better optical stability.

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基于多尺度融合卷积神经网络的微光图像增强算法

Liu Weiqiang,

Zhao Peng,

Song Xiangying

2023

激光与光电子学进展

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To overcome the problems of poor generalizability and an inability to adapt to complex real scenes of traditional lowlight image enhancement algorithms, a new method based on multiscale concat convolutional neural network is proposed here. This method achieves lowlight image enhancement by learning the mapping relationship between lowlight and normal images. Taking the lowlight image as input, the shallow layer information of the image is extracted through the preprocessing module. Then, Selective Kernel Network (SKNet) is fused to the local path to form a feature extraction network. Finally, the global feature is fused with the local feature, which is obtained by weight learning of the feature map with a channel attention module. Bilateral guided upsampling is used to restore the image size and obtain the mapping function of the lowlight image, after which the image enhancement is completed. Based on the MIT -Adobe 5K dataset, a comparative experiment with nine other advanced methods showed that the proposed method can effectively improve the brightness and details of lowlight images. Hence, the proposed method is superior to other contrast algorithms in terms of visual effects and quantitative evaluation.

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基于对抗生长的目标检测方法

Cited by 3 publications

References 0 publications

基于隐私保护机制的辐射光源衍射图像筛选

基于隐私保护机制的辐射光源衍射图像筛选

基于光场统计的机场飞鸟目标检测

基于多尺度融合卷积神经网络的微光图像增强算法

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