{"version":1,"type":"story","url":"https://digestai.news/story/paani-delivers-on-device-visual-evidence-fusion-for-river-robots","json":"https://digestai.news/story/paani-delivers-on-device-visual-evidence-fusion-for-river-robots.json","markdown":"https://digestai.news/story/paani-delivers-on-device-visual-evidence-fusion-for-river-robots.md","slug":"paani-delivers-on-device-visual-evidence-fusion-for-river-robots","headline":"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.\n\nThe 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.\n\nThe 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.","keyPoints":["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"],"whyItMatters":"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.","category":{"slug":"models","name":"Generative AI & Models","url":"https://digestai.news/category/models"},"entities":{"companies":[],"models":["YOLO11n","MobileNetV3 Small","PAANI"],"people":[]},"firstPublishedAt":"2026-09-22T04:00:00Z","updatedAt":"2026-09-22T04:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"arXiv cs.AI","title":"PAANI : On Device Visual Evidence Fusion and Explainable Guidance for River Robot Simulation","url":"https://arxiv.org/abs/2609.22353","publishedAt":"2026-09-22T04:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"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","publisher":"Digest AI","title":"PAANI delivers on-device visual evidence fusion for river robots","datePublished":"2026-09-22T04:00:00Z","url":"https://digestai.news/story/paani-delivers-on-device-visual-evidence-fusion-for-river-robots"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}