Evidence map›Paper›PMID 40732451›Full record

ArticleSensors (Basel, Switzerland)2025

Development of an Automated Crack Detection System for Port Quay Walls Using a Small General-Purpose Drone and Orthophotos.

Daiki Komi, Daisuke Yoshida, Tomohito Kameyama

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Daiki KomiGraduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, Japan.
Daisuke YoshidaGraduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, Japan.ORCID 0009-0002-3362-1105
Tomohito KameyamaGraduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, Japan.

Funding

Japan Science and Technology Agency JPMJPF2115Japan Society for the Promotion of Science JP23K11339
6 · The paper itself

Abstract

Aging port infrastructure demands frequent and reliable inspections, yet the existing automated systems often require expensive industrial drones, posing significant adoption barriers for local governments with limited resources. To address this challenge, this study develops a low-cost, automated crack detection system for port quay walls utilizing orthophotos generated from a small general-purpose drone. The system employs the YOLOR (You Only Learn One Representation) object detection algorithm, enhanced by two novel image processing techniques-overlapping tiling and pseudo-altitude slicing-to overcome the resolution limitations of low-cost cameras. While official guidelines for port facilities designate 3 mm as an inspection threshold, our system is specifically designed to achieve a higher-resolution detection capability for cracks as narrow as 1 mm. This approach ensures reliable detection with a sufficient safety margin and enables the proactive monitoring of crack progression for preventive maintenance. The effectiveness of the proposed image processing techniques was validated, with an

Indexed as

crack detectiondeep learningorthophotoport quay wallsmall general-purpose drone

Identifiers

PMID40732451
PMCPMC12300741

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.