RCBS-ReID: Rank-Reversal-Aware Compression and Budget-Adaptive Search for Efficient Edge Wooden Pallet Re-Identification

Wooden pallet re-identification (ReID) is essential for reliable logistics traceability and warehouse management. Although deep ReID models have achieved promising retrieval accuracy, practical deployment remains limited by high-dimensional gallery descriptor storage and full-gallery matching latency. Existing feature compression methods mainly reduce descriptor size, but compression-induced approximation errors may disturb or even reverse the ranking order between positive samples and hard negatives. Meanwhile, many retrieval acceleration methods rely on fixed search and reranking budgets, causing redundant computation for easy queries and missed relevant candidates for hard queries. To address these limitations, we propose RCBS-ReID, a rank-reversal-aware compression and budget-adaptive search framework for efficient wooden pallet ReID. Specifically, Rank-Reversal-Aware Compact Encoding (RACE) estimates rank-reversal risk to guide compact subspace selection, reducing gallery storage while preserving retrieval ranking stability after compression. In addition, Dual Budget Adaptive Search (DBAS) combines coarse candidate recall with fine reranking, and adaptively allocates search and reranking budgets according to query difficulty to reduce redundant retrieval cost. Experiments on an in-house wooden pallet ReID dataset from real logistics scenarios show that RCBS-ReID achieves superior overall performance compared with 15 representative ReID methods. Compared with the baseline, RCBS-ReID reduces gallery storage by 95.0%, achieves 3.23× retrieval acceleration, and improves mAP by 1.11%.