Tiny Collaborative Inference for Occlusion-Robust Object Detection

• URL: http://arxiv.org/abs/2606.02894v2
• Date: Wed, 03 Jun 2026 10:50:50 GMT
• Title: Tiny Collaborative Inference for Occlusion-Robust Object Detection
• Abstract summary: Edge AI nodes for search and rescue are increasingly expected to run computer vision locally.<n>Ultra-low-end hardware imposes hard constraints on memory, compute, and inter-device communication.<n>This work addresses occlusion-robust object detection devices with less than 1 MB by combining an MCU backbone, a YOLOv2 detection head, and Lite quantisation.
• Score: 0.5352699766206808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 fusion to three views improves accuracy further (up to +0.3827 mAP) at modest communication overhead (~1.3 KB per exchange). Hardware experiments progress from a host-assisted USB-relay baseline to a Wi-Fi peer-to-peer deployment on two Coral Dev Board Micro units, where WBF executes on-device with negligible communication energy relative to inference. In a 301.9 s autonomous session of 108 frames, fused output is produced on 61 frames versus 47 for a single board - a coverage gain of +29.8%. A decentralised federated learning feasibility note is included but not treated as a primary result, as performance remains limited under non-iid data. The results support decision-level fusion as a viable option for improving occlusion robustness in small-scale edge object detection, including host-free multi-board operation on ultra-low-end hardware.

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