Duplicate detection of real-world objects detected by UAV swarm 

Any pipeline processing drone inputs for analytics in the cloud must scale to dozens of objects detected per image for ten thousands of image from each drone in a UAV swarm of say 2000 drones with a reasonable latency so that objects can be catalogued without backlog and bottleneck of duplicate detection. Exhaustive matching of every new detection against a massive catalog quickly becomes prohibitive as drone swarm size and data volume grow. To reduce the cost per new detection effectively while maintaining timely cataloging, several advanced strategies can be employed in the cloud: 

1. Hierarchical Indexing and Partitioning 
   Partition the knowledge graph and object catalog spatially or semantically. By limiting comparison to relevant graph partitions or geographic regions, the search space for duplicates shrinks drastically, reducing computational load and latency. 

2. Approximate Nearest Neighbor (ANN) Search 
   Instead of exact exhaustive matching, using fast ANN algorithms on learned embeddings allows quick retrieval of the most relevant candidate duplicates without scanning the entire catalog. Methods like HNSW or Faiss are effective for billion-scale vector search. 

3. Incremental or Streaming Graph Updates 
   Maintain a continuously updated incremental knowledge graph rather than reprocessing the entire catalog upon each new detection. This allows constant-time deduplication against recent updates, distributing workload evenly. 

4. Candidate Filtering with Confidence Thresholds 
   Use the detection confidence and semantic embedding similarity thresholds to early-filter out unlikely duplicates, reducing detailed comparisons to a smaller candidate set. 

5. Leveraging Distributed and Serverless Architectures 
   Utilize cloud-native scalable databases and parallel compute clusters to process different batches of detections concurrently across graph partitions with overlapped I/O. 

6. Batch Processing and Temporal Aggregation 
   Group incoming detections per time window or image batch to jointly analyze similarities and merge duplicates at once, amortizing query cost. 

7. Leveraging metadata or context for duplicate elimination before doing vector search. Metadata such as location, timestamp, local (drone world) to global (real-world) mapping, digital signatures, transforms and such other attributes can help alleviate the cost incurred from a vector only search. 

These strategies mitigate backlog risk, improve throughput performance, and reduce cost per detection significantly. Overall, moving from naive exhaustive matching to a carefully architected graph querying and filtering pipeline is essential for scalable real-time cataloging over large UAV swarms.  

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