A class of augmented complex-value FLANN adaptive algorithms for nonlinear systems

Luo, Zheng-Yan; Zhou, Jiliu; Pu, YoungGun; Li, Lei

doi:10.1016/j.neucom.2022.11.047

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…(4) The detection results in the rotating annotated SAR light image construction dataset show that, compared with the original algorithm, the improved algorithm proposed in this paper improves the detection accuracy and detection speed of Minnan folk light animation and proves the feasibility of a lightweight network for building detection directions in SAR images. In the future, the use of more advanced algorithms for the extraction of features from specific parts of the building will be a worthwhile area of research and could include hybrid heuristics [30], metaheuristics, adaptive algorithms [31], self-adaptive algorithms [32] and island algorithms. We also found that there are many different fields that are applying advanced algorithms to resolve their respective problems, such as online learning [33], scheduling [34], multi-objective optimization [35], transportation, medicine [36], data classification, etc.…”

Section: Discussionmentioning

confidence: 99%

Application of Minnan Folk Light and Shadow Animation in Built Environment in Object Detection Algorithm

Wu¹,

Huang²,

Xiong

et al. 2023

Buildings

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To resolve the problems of deep convolutional neural network models with many parameters and high memory resource consumption, a lightweight network-based algorithm for building detection of Minnan folk light synthetic aperture radar (SAR) images is proposed. Firstly, based on the rotating target detection algorithm R-centernet, the Ghost ResNet network is constructed to reduce the number of model parameters by replacing the traditional convolution in the backbone network with Ghost convolution. Secondly, a channel attention module integrating width and height information is proposed to enhance the network’s ability to accurately locate salient regions in folk light images. Content-aware reassembly of features (CARAFE) up-sampling is used to replace the deconvolution module in the network to fully incorporate feature map information during up-sampling to improve target detection. Finally, the constructed dataset of rotated and annotated light and shadow SAR images is trained and tested using the improved R-centernet algorithm. The experimental results show that the improved algorithm improves the accuracy by 3.8%, the recall by 1.2% and the detection speed by 12 frames/second compared with the original R-centernet algorithm.

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