# PAANI delivers on-device visual evidence fusion for river robots

Digest AI · Generative AI & Models · published 2026-09-22T04:00:00Z

Canonical: https://digestai.news/story/paani-delivers-on-device-visual-evidence-fusion-for-river-robots

## Summary

PAANI is an on‑device perception‑to‑guidance system designed for mobile river monitoring robots. It fuses a YOLO11n object detector and a MobileNetV3 Small semantic segmenter on an Arduino UNO Q, producing explainable advisories that expose the evidence behind each decision.

The architecture was trained on 10,000 WaterScenes images for four‑class detection and 1,127 MaSTr1325 images for segmentation, with 198 validation images. The FP32 ONNX models occupy 14.817 MB. In tests the detector achieved a mean average precision of 0.7388 at 0.5 IoU, while the exported model scored 0.7367. The segmenter reached a mean intersection‑over‑union of 0.9750. A five‑minute recording on the UNO Q yielded median pipeline latency of 467.8 ms and a 95th percentile of 580.3 ms at a 0.5 Hz cadence.

The study highlights the feasibility of inspectable, reusable edge‑robotics foundations that separate model accuracy from on‑board execution, and it points out issues such as black‑input misclassification and sampling‑rate mismatches that can affect real‑time guidance.

## Key points

- PAANI fuses YOLO11n and MobileNetV3 Small on Arduino UNO Q, 14.817 MB models
- Median latency 467.8 ms, 95th percentile 580.3 ms at 0.5 Hz cadence
- Detector mAP 0.7388, segmenter mIoU 0.9750

## Why it matters

Edge AI for river monitoring enables autonomous robots to interpret obstacles and boundaries in real time, improving safety and reducing reliance on GPS or external sensors.

## Sources

1. [PAANI : On Device Visual Evidence Fusion and Explainable Guidance for River Robot Simulation](https://arxiv.org/abs/2609.22353) (arXiv cs.AI, 2026-09-22, primary source)

## Cite

Digest AI, "PAANI delivers on-device visual evidence fusion for river robots", 22 September 2026, https://digestai.news/story/paani-delivers-on-device-visual-evidence-fusion-for-river-robots

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