Tree extraction from multi-scale UAV images using Mask R-CNN with FPN

Öçer, Nuri Erkin; Kaplan, Gordana; Erdem, Fırat; Matcı, Dilek Küçük; Avdan, Uğur

doi:10.1080/2150704x.2020.1784491

Cited by 79 publications

(36 citation statements)

References 25 publications

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“…The use of TLS as a calibration tool for drone-LiDAR [48] would allow the exploitation of three-dimensional LiDAR data rather than relying on two dimensional crown statistics, providing one approach to improve allometry. As savanna tree allometry is known to vary among species [49], further improvements could be achieved by first classifying species, for example through the application of deep learning techniques such as mask regional convolutional neural networks (mask R-CNN), see [50], then developing species-specific allometric models. While more accurate allometry would improve the relationship between modelled and field-derived DBH, an argument can also be made for moving away from DBH altogether.…”

Section: Discussionmentioning

confidence: 99%

Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR

Rudge

Levick

Bartolo³

et al. 2021

Remote Sensing

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The diameter distribution of savanna tree populations is a valuable indicator of savanna health because changes in the number and size of trees can signal a shift from savanna to grassland or forest. Savanna diameter distributions have traditionally been monitored with forestry techniques, where stem diameter at breast height (DBH) is measured in the field within defined sub-hectare plots. However, because the spatial scale of these plots is often misaligned with the scale of variability in tree populations, there is a need for techniques that can scale-up diameter distribution surveys. Dense point clouds collected from uncrewed aerial vehicle laser scanners (UAV-LS), also known as drone-based LiDAR (Light Detection and Ranging), can be segmented into individual tree crowns then related to stem diameter with the application of allometric scaling equations. Here, we sought to test the potential of UAV-LS tree segmentation and allometric scaling to model the diameter distributions of savanna trees. We collected both UAV-LS and field-survey data from five one-hectare savanna woodland plots in northern Australia, which were divided into two calibration and three validation plots. Within the two calibration plots, allometric scaling equations were developed by linking field-surveyed DBH to the tree metrics of manually delineated tree crowns, where the best performing model had a bias of 1.8% and the relatively high RMSE of 39.2%. A segmentation algorithm was then applied to segment individual tree crowns from UAV-LS derived point clouds, and individual tree level segmentation accuracy was assessed against the manually delineated crowns. 47% of crowns were accurately segmented within the calibration plots and 68% within the validation plots. Using the site-specific allometry, DBH was modelled from crown metrics within all five plots, and these modelled results were compared to field-surveyed diameter distributions. In all plots, there were significant differences between field-surveyed and UAV-LS modelled diameter distributions, which became similar at two of the plots when smaller trees (<10 cm DBH) were excluded. Although the modelled diameter distributions followed the overall trend of field surveys, the non-significant result demonstrates a need for the adoption of remotely detectable proxies of tree size which could replace DBH, as well as more accurate tree detection and segmentation methods for savanna ecosystems.

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Section: Discussionmentioning

confidence: 99%

Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR

Rudge

Levick

Bartolo³

et al. 2021

Remote Sensing

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“…The mapping and detection of individual tree crowns, tree/plant/vegetation species, crops, and wetlands from UAV-based images are achieved by diverse CNN architectures, which are used to perform different tasks, including path-based classification [78][79][80][81][82][83][84][85][86][87], object detection [88][89][90][91][92][93][94][95][96][97], and semantic segmentation [98][99][100][101][102][103][104][105][106][107]. Recently, semantic segmentation, a commonly used term in computer vision where each pixel within the input imagery is assigned to a particular class, has been a widely used technique in diverse earth-related applications [108].…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

et al. 2021

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Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

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“…en, the fixed convolution layer and pooling layer are replaced with deformable convolution layer and deformable pooling layer. Finally, the FPN [28][29][30] network is used for multifeature fusion, and the soft nonmaximum suppression (soft NMS) [31] is used to reduce the confidence of the detection frame larger than the threshold, so as to alleviate the situation of the target missing detection. According to the test results on the open dataset NEU-DET, the proposed algorithm can effectively detect a variety of defects on steel surface, which is higher than the ordinary steel surface detection algorithms in accuracy.…”

Section: Introductionmentioning

confidence: 99%

A New Steel Defect Detection Algorithm Based on Deep Learning

Zhao

Feng

Huang

et al. 2021

Computational Intelligence and Neuroscience

153

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In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.

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Tree extraction from multi-scale UAV images using Mask R-CNN with FPN

Cited by 79 publications

References 25 publications

Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR

Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

A New Steel Defect Detection Algorithm Based on Deep Learning

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