scholarly journals Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3595 ◽  
Author(s):  
Anderson Aparecido dos Santos ◽  
José Marcato Junior ◽  
Márcio Santos Araújo ◽  
David Robledo Di Martini ◽  
Everton Castelão Tetila ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Spatial Resolution ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Target Object ◽  
Detection Methods ◽  
Individual Tree ◽  
Art Object

Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.

scholarly journals INDIVIDUAL TREE SPECIES CLASSIFICATION BASED ON TERRESTRIAL LASER SCANNING USING CURVATURE ESTIMATION AND CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1077-1082
Author(s):  
T. Mizoguchi ◽  
A. Ishii ◽  
H. Nakamura
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tree Species ◽  
Point Cloud ◽  
Superior Performance ◽  
Depth Image ◽  
Species Classification ◽  
Individual Tree ◽  
Curvature Estimation ◽  
Bark Texture

<p><strong>Abstract.</strong> In this paper, we propose a new method for specifying individual tree species based on depth and curvature image creation from point cloud captured by terrestrial laser scanner and Convolutional Neural Network (CNN). Given a point cloud of an individual tree, the proposed method first extracts the subset of points corresponding to a trunk at breast-height. Then branches and leaves are removed from the extracted points by RANSAC -based circle fitting, and the depth image is created by globally fitting a cubic polynomial surface to the remaining trunk points. Furthermore, principal curvatures are estimated at each scanned point by locally fitting a quadratic surface to its neighbouring points. Depth images clearly capture the bark texture involved by its split and tear-off, but its computation is unstable and may fail to acquire bark shape in the resulting images. In contrast, curvature estimation enables stable computation of surface concavity and convexity, and thus it can well represent local geometry of bark texture in the curvature images. In comparison to the depth image, the curvature image enables accurate classification for slanted trees with many branches and leaves. We also evaluated the effectiveness of a multi-modal approach for species classification in which depth and curvature images are analysed together using CNN and support vector machine. We verified the superior performance of our proposed method for point cloud of Japanese cedar and cypress trees.</p>

Lidar-based individual tree species classification using convolutional neural network

2017 ◽  
Author(s):  
Tomohiro Mizoguchi ◽  
Akira Ishii ◽  
Hiroyuki Nakamura ◽  
Tsuyoshi Inoue ◽  
Hisashi Takamatsu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tree Species ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification

scholarly journals Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Luciene Sales Dagher Arce ◽  
Mauro dos Santos de Arruda ◽  
Danielle Elis Garcia Furuya ◽  
Lucas Prado Osco ◽  
Ana Paula Marques Ramos ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Tree Species ◽  
State Of The Art ◽  
Palm Tree ◽  
Forest Environment ◽  
Individual Tree ◽  
Palm Trees ◽  
Rgb Images

Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. State-of-the-art deep learning methods could be capable of identifying tree species with an attractive cost, accuracy, and computational load in RGB images. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment, and considers the likelihood of every pixel in the image to be recognized as a possible tree by implementing a confidence map feature extraction. This study compares the performance of the proposed method against state-of-the-art object detection networks. For this, images from a dataset composed of 1,394 airborne scenes, where 5,334 palm-trees were manually labeled, were used. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than both Faster R-CNN and RetinaNet considering equal experiment conditions. The proposed network provided fast solutions to detect the palm trees, with a delivered image detection of 0.073 seconds and a standard deviation of 0.002 using the GPU. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M flexuosa palm tree and may be useful for future frameworks.

scholarly journals Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luciene Sales Dagher Arce ◽  
Lucas Prado Osco ◽  
Mauro dos Santos de Arruda ◽  
Danielle Elis Garcia Furuya ◽  
Ana Paula Marques Ramos ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tree Species ◽  
Single Species ◽  
Absolute Error ◽  
Palm Tree ◽  
Forest Environment ◽  
Individual Tree ◽  
Palm Trees ◽  
Mauritia Flexuosa

AbstractAccurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than Faster R-CNN and RetinaNet methods considering equal experiment conditions. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M. flexuosa palm tree and may be useful for future frameworks.

scholarly journals Mauritia Flexuosa Palm-Trees Airborne Mapping with Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Luciene Sales Daguer Arce ◽  
Lucas Prado Osco ◽  
Mauro dos Santos Arruda ◽  
Danielle Ellis Garcia Furuya ◽  
Ana Paula Marques Ramos ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tree Species ◽  
Single Species ◽  
Absolute Error ◽  
Palm Tree ◽  
Forest Environment ◽  
Individual Tree ◽  
Palm Trees ◽  
Mauritia Flexuosa

Abstract Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than Faster R-CNN and RetinaNet methods considering equal experiment conditions. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M flexuosa palm tree and may be useful for future frameworks.

scholarly journals Object Detection Combining CNN and Adaptive Color Prior Features

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2796
Author(s):  
Peng Gu ◽  
Xiaosong Lan ◽  
Shuxiao Li
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Design Method ◽  
Detection Methods ◽  
Data Sets ◽  
Test Image ◽  
Detection Algorithms ◽  
Benchmark Data ◽  
Color Features

When compared with the traditional manual design method, the convolutional neural network has the advantages of strong expressive ability and it is insensitive to scale, light, and deformation, so it has become the mainstream method in the object detection field. In order to further improve the accuracy of existing object detection methods based on convolutional neural networks, this paper draws on the characteristics of the attention mechanism to model color priors. Firstly, it proposes a cognitive-driven color prior model to obtain the color prior features for the known types of target samples and the overall scene, respectively. Subsequently, the acquired color prior features and test image color features are adaptively weighted and competed to obtain prior-based saliency images. Finally, the obtained saliency images are treated as features maps and they are further fused with those extracted by the convolutional neural network to complete the subsequent object detection task. The proposed algorithm does not need training parameters, has strong generalization ability, and it is directly fused with convolutional neural network features at the feature extraction stage, thus has strong versatility. Experiments on the VOC2007 and VOC2012 benchmark data sets show that the utilization of cognitive-drive color priors can further improve the performance of existing object detection algorithms.

scholarly journals Geometric property-based convolutional neural network for indoor object detection

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199332
Author(s):  
Xintao Ding ◽  
Boquan Li ◽  
Jinbao Wang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Geometric Property ◽  
Ground Truth ◽  
Geometric Constraints ◽  
Depth Information ◽  
Training Set ◽  
Object Knowledge ◽  
The Mean

Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropriate anchors for indoor objects. After the anchors are regressed to the regions of interest produced by a region proposal network (RPN-RoIs), we then use 2D geometric constraints to refine the RPN-RoIs, in which the 2D constraint of every classification is a convex hull region enclosing the width and height coordinates of the ground-truth boxes on the training set. Comparison experiments are implemented on two indoor datasets SUN2012 and NYUv2. Since the depth information is available in NYUv2, we involve depth constraints in GP-Faster and propose 3D geometric property-based Faster R-CNN (DGP-Faster) on NYUv2. The experimental results show that both GP-Faster and DGP-Faster increase the performance of the mean average precision.

scholarly journals The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1737
Author(s):  
Wooseop Lee ◽  
Min-Hee Kang ◽  
Jaein Song ◽  
Keeyeon Hwang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Processing Speed ◽  
Model Comparison ◽  
Distance Estimation ◽  
Visual Object ◽  
Suitable Model ◽  
Automated Vehicles ◽  
Automated Driving

As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types.

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