Street tree segmentation from mobile laser scanning data using deep learning-based image instance segmentation

Li, Qiujie; Yan, Yu

doi:10.1016/j.ufug.2023.128200

Urban Forestry & Urban Greening

2024

DOI: 10.1016/j.ufug.2023.128200

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Street tree segmentation from mobile laser scanning data using deep learning-based image instance segmentation

Qiujie Li,

Yu Yan

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2024

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Cited by 7 publications

References 27 publications

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Poplar seedling varieties and drought stress classification based on multi-source, time-series data and deep learning

Wang,

Zhang,

Bian

et al. 2024

Industrial Crops and Products

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Poplar seedling varieties and drought stress classification based on multi-source, time-series data and deep learning

Wang,

Zhang,

Bian

et al. 2024

Industrial Crops and Products

View full text Add to dashboard Cite

Street tree segmentation method combining image semantic segmentation and point cloud clustering

Zhu,

Song,

Gao

et al. 2024

J. Phys.: Conf. Ser.

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As a common object in the park scene, the 3D information of street trees is crucial for digital cities, and extracting individual trees from point cloud data accurately and efficiently is a hotspot and a difficult point-in-point cloud processing. Aiming at the interference problem of complex non-tree objects and dynamic objects in the park environment, a tree point cloud segmentation method combining a deep learning network framework with a point cloud clustering algorithm is proposed in this paper. First, this paper applies the YOLOv8 to the semantic segmentation of camera images, and fuses image semantics with point cloud data, to build the semantic point cloud model. Then, preliminary extraction for street trees is performed using the semantic information of this point cloud model, and radius filtering is used to reduce the outliers. Finally, the individual tree segmentation is realized using an enhanced multi-level Euclidean clustering algorithm based on KD-Tree. Experimental results indicate that the proposed method performs well in extracting and segmenting the roadway trees, with a recall of 94.5%, a precision of 80.7%, and an F1-score of 87.0%.

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Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms

Fan,

Wang,

Cui

et al. 2024

Foods

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Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. A new dataset of high moisture content maize kernel phenotypic features was constructed by extracting seven features (geometric and shape features). Then, the regression model of the kernel (broken and unbroken) weight prediction and the classification model of kernel defect detection were established using the mainstream machine learning algorithm. In this way, the defect rapid identification and accurate weight prediction of broken kernels achieve the purpose of broken rate quantitative detection. The results prove that LGBM (light gradient boosting machine) and RF (random forest) algorithms were suitable for constructing weight prediction models of broken and unbroken kernels, respectively. The r values of the models built by the two algorithms were 0.985 and 0.910, respectively. SVM (support vector machine) algorithms perform well in constructing maize kernel classification models, with more than 95% classification accuracy. A strong linear relationship was observed between the predicted and actual broken rates. Therefore, this method could help to be an accurate, objective, efficient broken rate online detection method for maize harvest.

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Street tree segmentation from mobile laser scanning data using deep learning-based image instance segmentation

Cited by 7 publications

References 27 publications

Poplar seedling varieties and drought stress classification based on multi-source, time-series data and deep learning

Poplar seedling varieties and drought stress classification based on multi-source, time-series data and deep learning

Street tree segmentation method combining image semantic segmentation and point cloud clustering

Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms

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