Abstract
In recent years, targeted spraying technology, which was proposed to solve the problems of pesticide waste and environmental pollution caused by traditional spraying methods, has been successfully applied in orchards. In street scenes with a variety of object classes, it is challenging to detect tree crowns, which limits the application of targeted spraying for street trees. Two-dimensional (2D) light detection and ranging (LiDAR) sensors have been widely used in targeted spraying to monitor the presence of tree crowns. Considering a mobile laser scanning (MLS) system with a single 2D LiDAR sensor in push-broom mode, this paper proposes a pointwise method for street tree crown detection from MLS point clouds by using a grid index and local features. First, an efficient two-level neighbourhood search method is proposed to obtain the spherical neighbourhood of a single point by using the grid index of the MLS point clouds. Subsequently, a set of local statistical features, including width features, depth features, elevation features, intensity features, echo number features, dimensionality features and a density feature, are extracted from the spherical neighbourhood. Finally, a supervised learning algorithm called boosting is used to automatically fuse these features and generate a pointwise tree crown detector from a labelled training set. An MLS point cloud with 15,134,000 points is captured from both sides of a 136.5 m street, and the cloud contains buildings, lanes, sidewalks, benches, street lights, bicycles, traffic signs, grids, trees, bushes, turf areas, parterres, and pedestrians. The estimated Bayesian errors of single-feature approaches range from 6.23 to 36.09%, and the error rate of the tree crown detector composed of all features is less than 0.73%, with a recall rate of over 98.30% and a precision of over 99.13%. The experimental results show that the proposed method can provide an online, fine and accurate protocol for targeted spraying.
Zusammenfassung
Erkennung von Straßenbaumkronen mit mobilen Laserscannerdaten unter Verwendung eines Rasterindexes und lokaler Merkmale. In den letzten Jahren wurde die Technologie des gezielten Sprühens, die eingeführt wurde, um die Probleme der Pestizidabfälle und der Umweltverschmutzung, die durch die traditionellen Sprühmethoden verursacht werden, zu lösen, erfolgreich in Obstplantagen eingesetzt. In Straßenszenen mit einer Vielzahl von Objektklassen ist es schwierig, Baumkronen zu erkennen, was die Anwendung des gezielten Sprühens für Straßenbäume einschränkt. Zweidimensionale (2D) Light Detection and Ranging (LiDAR)-Sensoren wurden bei der gezielten Besprühung zur Überwachung von Baumkronen häufig eingesetzt. Unter Berücksichtigung eines mobilen Laserscanning-Systems (MLS) mit einem einzelnen 2D-LiDAR-Sensor im Push-Broom-Modus wird in diesem Beitrag eine punktweise Methode zur Erkennung von Straßenbaumkronen aus MLS-Punktwolken unter Verwendung eines Gitterindexes und lokaler Merkmale vorgeschlagen. Zunächst wird eine effiziente zweistufige Nachbarschaftssuchmethode vorgeschlagen, um die sphärische Nachbarschaft eines einzelnen Punktes mit Hilfe des Gitterindexes der MLS-Punktwolken zu ermitteln. Anschließend werden aus der sphärischen Nachbarschaft eine Reihe lokaler statistischer Merkmale, darunter Breitenmerkmale, Tiefenmerkmale, Höhenmerkmale, Intensitätsmerkmale, Echonummernmerkmale, Dimensionalitätsmerkmale und ein Dichtemerkmal, extrahiert. Schließlich wird ein überwachter Lernalgorithmus namens Boosting verwendet, um diese Merkmale automatisch zu fusionieren und einen punktweisen Baumkronendetektor aus einem gelabelten Trainingssatz zu erzeugen. Eine MLS-Punktwolke mit 15.134.000 Punkten wurde von beiden Seiten einer 136,5 m langen Straße erfasst, und diese Punktwolke enthält Gebäude, Fahrbahnen, Gehwege, Bänke, Straßenlampen, Fahrräder, Verkehrsschilder, Gitter, Bäume, Büsche, Rasenflächen und Fußgänger. Die geschätzten Bayes'schen Fehler von Einzelmerkmalen liegen zwischen 6,23 und 36,09 %, und die Fehlerrate des aus allen Merkmalen zusammengesetzten Baumkronendetektors beträgt weniger als 0,73 %, bei einer Recall (Vollständigkeit)-Rate von über 98,30 % und einer Präzision (Korrektheit) von über 99,13 %. Die experimentellen Ergebnisse zeigen, dass die vorgeschlagene Methode ein feines und genaues Vorgehen für gezieltes Sprühen liefern kann.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 31901239) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20170930).
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Li, Q., Li, X., Tong, Y. et al. Street Tree Crown Detection with Mobile Laser Scanning Data Using a Grid Index and Local Features. PFG 90, 305–317 (2022). https://doi.org/10.1007/s41064-022-00208-w
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DOI: https://doi.org/10.1007/s41064-022-00208-w