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- W4387541062 abstract "Based on YOLOX network, this paper presented an algorithm for extracting point-like independent houses from remote sensing images. First, an Adaptively Spatial Feature Fusion (ASFF) network was added to the feature extraction module PANet to deeply mine the detailed features of small target houses with different scales. Second, a feature extraction module based on ECA local cross-channel interaction attention mechanism was designed. Efficient channel interaction paid more attention to the positive sample feature information in the feature map and lowered the complicacy of the model. Finally, the Swish activation function was used to avert poor activation effect. Experiments were conducted on the point-like independent houses data set, and the optimum mechanism and effectiveness of the improved method were validated by qualitative analysis of ablation experiments and quantitative analysis of comparison experiments. On the premise of adding ASFF mechanism and ECA attention mechanism and optimizing Swish activation function, the mAP precision of the improved network model was up to 94.83%, 11.16% higher than that of the original network. The robustness and effectiveness of the improved method were quantitatively verified by conducting comparative experiments with widely used detection algorithms." @default.
- W4387541062 created "2023-10-12" @default.
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- W4387541062 date "2023-10-11" @default.
- W4387541062 modified "2023-10-12" @default.
- W4387541062 title "Algorithm for extracting point-like independent houses from remote sensing images based on YOLOX" @default.
- W4387541062 doi "https://doi.org/10.1117/12.3004156" @default.
- W4387541062 hasPublicationYear "2023" @default.
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