{"ok":true,"nota":{"id":"ceb1b4bcd2ea5665","slug":"revision-de-175-estudios-sobre-deteccion-de-grietas-con-ia-en-drones-y-ceb1b4","titulo":"Revisión de 175 estudios sobre detección de grietas con IA en drones y dispositivos de campo","resumen":"Los autores publicaron las tablas de evidencia y los archivos de auditoría de una revisión crítica sobre redes neuronales que detectan grietas en estructuras con drones. Incluyen la lista de 175 estudios analizados, la clasificación de su grado de evidencia y un registro de brechas.","fuente":"zenodo","fuenteNombre":"Zenodo · Investigación abierta","color":"#1682d4","autor":"Singh, Thokchom Chittaranjan, Binodini, Pebam, Laishram, Romesh","url":"https://doi.org/10.5281/zenodo.23452747","imagen":"/hoy/portada/ceb1b4bcd2ea5665","tema":"tecnologia","subtemas":["deteccion de grietas","drones","vision por computadora","infraestructura"],"entidades":["Zenodo"],"formato":"dato","entretenimiento":false,"pubTs":"2026-10-21T03:59:19.000Z","indexTs":"2026-10-23T00:09:06.520Z","hash":"b649fa080482aed0a483bcf6dfadda3e523613aad2c7222d47d081042c1c313a","tsa":{"time":"Oct 23 00:09:48 2026 GMT","provider":"freetsa.org","sobre":"sello"},"stats":{"views":0,"likes":0,"clicks":0,"dwell":0},"tipo":"normal","registro":"hallazgo","porQueImporta":"Revisar 175 trabajos permite ver qué sistemas de detección de grietas funcionaron en condiciones reales y cuáles solo en laboratorio. Esto importa para inspeccionar puentes, represas y edificios en un país con mucha infraestructura envejecida. Según el propio material, el análisis se basa solo en el subconjunto de textos completos recuperados, no en toda la literatura.","quienFirma":"Zenodo (autores individuales, sin institución identificada)","confianza":"revisado","consulta":null},"sello":{"hash":"b649fa080482aed0a483bcf6dfadda3e523613aad2c7222d47d081042c1c313a","sello":"16aa64f4bdb1c1ed5929523602b3c0f8b245da5767b6f21cf14e0f8f2434f647","beacon":{"drand":{"cadena":"8990e7a9aaed2ffed73dbd7092123d6f289930540d7651336225dc172e51b2ce","ronda":6576105,"aleatorio":"9a752f53bdf8b20e7a2629cb1cd0be08103c236667169513abfd5a9adf48fa26","tiempo":"2026-10-23T00:09:30.000Z"},"nist":{"cadena":2,"pulso":1995252,"salida":"C400C5E71527B668D0BE133833C9446C2864D6A4B7B3FA89C3505D319CF61F8E9A1971E04B23EAE22ABDD617A97294AF2D049BC7FD9A6AC6AE6D3BD9F6B72DA7","tiempo":"2026-10-23T00:09:00.000Z"},"noAntes":"2026-10-23T00:09:30.000Z"},"verificacion":{"drand":true,"nist":true},"tsa":{"time":"Oct 23 00:09:48 2026 GMT","provider":"freetsa.org","sobre":"sello"},"original":{"titulo":"Evidence matrix and audit files for a critical survey of deep-learning crack detection and segmentation for UAV and Edge AI","descripcion":"<p>This deposit contains the evidence matrix and audit files supporting the critical survey &ldquo;Deep Learning-Based Structural Crack Detection and Segmentation for UAV and Edge AI Applications: A Critical Survey with Deployment-Evidence Grading&rdquo;. It includes a formula-driven evidence matrix, Supplementary Table S1 listing the 175 included studies (157 core and 18 supporting), the final Section 14 gap register, a duplicate-version crosswalk documenting the retired and updated records, a bibliography audit, and a reproducibility record. Counts apply to the retrieved full-text subset of the literature, not to the literature as a whole. Earlier working versions of the corpus are documented through the duplicate-version crosswalk. Screening and eligibility assessment were rule-based and AI-assisted under author-defined criteria, as described in the README and the manuscript. Seven ar","pubDate":"2026-10-21T03:59:19Z","url":"https://doi.org/10.5281/zenodo.23452747"},"indexado":"2026-10-23T00:09:06.520Z"}}