SDSHNet: Dynamic feature fusion with transformer and star operation for efficient detection in aluminum alloys microscopic inclusion

· · 来源:tools资讯

The efficiency depends on the query size relative to the data distribution. A small query in a sparse region prunes almost everything. A query that covers the whole space prunes nothing (because every node overlaps), degenerating to a brute-force scan. The quadtree gives you the most benefit when your queries are spatially local, which is exactly the common case for map applications, game physics, and spatial databases.

* Each solid progress should be committed in the git repository.

After testing

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