Tiny Collaborative Inference for Occlusion-Robust Object Detection
Edge AI nodes for search and rescue are increasingly expected to run computer vision locally, yet ultra-low-end hardware imposes hard constraints on memory, compute, and inter-device communication. This work addresses occlusion-robust object detection on devices with less than 1 MB SRAM by combining an MCUNet backbone, a YOLOv2 detection head, and Lite quantisation. Two collaborative inference strategies are evaluated: feature-level fusion, concatenating intermediate feature maps, and decision-level fusion via Weighted Boxes Fusion (WBF). WBF outperforms feature-level fusion under all tested occlusion conditions, yielding gains of up to +0.2736 mAP in asymmetric scenarios. Extending fusio...
| Authors | Chieh-Tung Cheng, Mustafa Aslanov, Eiman Kanjo |
|---|---|
| Primary category | cs.CV |
| Published | 1 June 2026 |
| Last updated at source | 3 June 2026 |
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