Measuring loblolly pine crowns with drone imagery through deep learning

Lou, Xiongwei; Huang, Yanxiao; Fang, Luming; Huang, Siqi; Gao, Haili; Yang, Laibang; Weng, Yuhui; Hung, I‐Kuai

doi:10.1007/s11676-021-01328-6

Cited by 31 publications

(14 citation statements)

References 30 publications

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“…Despite these works, more research is need, as there are no significant studies about this topic at ground level which focus on the detection of tree trunks with deep learning models and their evaluation with well-known metrics in the object detection domain. Furthermore, the majority of works related to forest tree detection are focused on performing the detection with Light Detection and Ranging (LiDaR) data alone [ 26 , 27 , 28 , 29 , 30 ], with aerial high-resolution multispectral imagery alone [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ] or with a combination of both [ 41 , 42 , 43 , 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

2021

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Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.

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Section: Introductionmentioning

confidence: 99%

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

2021

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“…While it is import to quantify some forest parameters such as DBH, tree height, and tree position, the measurement of tree crowns must not be underestimated, as it is quite difficult to assess this measure manually, and it provides a comprehension about the stand timber volume. For this, the authors in [14] made a study on methods for the detection and extraction of tree crowns from UAV-based images and further crown measurement. They used three DL models, namely Faster R-CNN, YOLOv3, and Single-Shot MultiBox Detector (SSD).…”

Section: Vision-based Perceptionmentioning

confidence: 99%

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

et al. 2021

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Robotics navigation and perception for forest management are challenging due to the existence of many obstacles to detect and avoid and the sharp illumination changes. Advanced perception systems are needed because they can enable the development of robotic and machinery solutions to accomplish a smarter, more precise, and sustainable forestry. This article presents a state-of-the-art review about unimodal and multimodal perception in forests, detailing the current developed work about perception using a single type of sensors (unimodal) and by combining data from different kinds of sensors (multimodal). This work also makes a comparison between existing perception datasets in the literature and presents a new multimodal dataset, composed by images and laser scanning data, as a contribution for this research field. Lastly, a critical analysis of the works collected is conducted by identifying strengths and research trends in this domain.

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“…With the continuous development of computer technology, object detection algorithm based on deep learning have achieved rapid development and has been widely used in autonomous driving, face recognition, crop disease and pest recognition, defect detection, and other fields [5][6][7]. There are two main types of object detection algorithms based on deep learning: One is a two-stage target detection algorithm that divides feature extraction and target localization into two stages, such as R-CNN [8,9] (Region Proposals for Convolutional Neural Networks), Fast R-CNN [10], and Faster R-CNN [11][12][13]. The second category is a one-stage target detection algorithm that integrates feature extraction and location processing, such as SSD [14] (Single Shot Multibox Detector) and YOLO [15,16] (You Only Look Once) series.…”

Section: Introductionmentioning

confidence: 99%

Research on Asphalt Pavement Disease Detection Based on Improved YOLOv5s

Zhugeng

Liang

2023

Journal of Sensors

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Pavement disease detection and classification is one of the key problems in computer vision and intelligent analysis. This is an automated target detection technology with great development potential, which can improve the detection efficiency of road management departments. The research based on the convolutional neural network is aimed at realizing asphalt pavement disease detection based on low resolution, occlusive interference, and complex environment. Considering the powerful function of the convolutional neural network and its successful application in object detection, we apply it to asphalt pavement disease detection, and the detection results are used for subsequent analysis and decision-making. At present, most of the research on pavement disease detection focuses on crack detection, and the detection of multiclass diseases is less, and its detection accuracy and speed need to be improved, which does not meet the actual engineering application. Therefore, a rapid asphalt pavement disease detection method based on improved YOLOv5s was proposed. The complex scene data enhancement technique was developed, which is used to enhance and extend the original data to improve the robustness of the model. The improved lightweight attention module SCBAM was integrated into the backbone network, which can enhance the feature extraction ability and improve the detection performance of the model for small targets. The spatial pyramid pooling was improved into SPPF to fuse the input features, which can solve the multiscale problem of the target and improve the reasoning efficiency of the model to a certain extent. The experimental results showed that, after the model is improved, the average accuracy of pavement disease reaches 94.0%. Compared with YOLOv5s, the precision of the improved YOLOv5-pavement is increased by 3.1%, the recall rate is increased by 4.4%, the F1 score is increased by 3.7%, and the mAP is increased by 3.8%. For transverse cracks, longitudinal cracks, mesh cracks, potholes, and repaired pavement, the detection accuracy of pavement disease detection method based on YOLOv5-pavement is improved by 3.4%, 3.1%, 4.0%, 7.5%, and 4.8%, respectively, compared with that based on YOLOv5s. The proposed method provides support for the detection work of pavement diseases.

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Measuring loblolly pine crowns with drone imagery through deep learning

Cited by 31 publications

References 30 publications

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

Research on Asphalt Pavement Disease Detection Based on Improved YOLOv5s

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