Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration

• URL: http://arxiv.org/abs/2410.00503v2
• Date: Sun, 6 Oct 2024 07:34:52 GMT
• Title: Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration
• Authors: Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield, Richard Green,
• Abstract summary: This research focuses on the development of a drone equipped with pruning tools and a stereo vision camera to accurately detect and measure the spatial positions of tree branches. YOLO is employed for branch segmentation, while two depth estimation approaches, monocular and stereo, are investigated.
• Score: 4.730379319834545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research focuses on the development of a drone equipped with pruning tools and a stereo vision camera to accurately detect and measure the spatial positions of tree branches. YOLO is employed for branch segmentation, while two depth estimation approaches, monocular and stereo, are investigated. In comparison to SGBM, deep learning techniques produce more refined and accurate depth maps. In the absence of ground-truth data, a fine-tuning process using deep neural networks is applied to approximate optimal depth values. This methodology facilitates precise branch detection and distance measurement, addressing critical challenges in the automation of pruning operations. The results demonstrate notable advancements in both accuracy and efficiency, underscoring the potential of deep learning to drive innovation and enhance automation in the agricultural sector.

Related papers

• Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Integrating SGBM and Segmentation Models [4.730379319834545]
  This research proposes the development of a drone-based pruning system equipped with specialized pruning tools and a stereo vision camera. Deep learning algorithms, including YOLO and Mask R-CNN, are employed to ensure accurate branch detection. The synergy between these techniques facilitates the precise identification of branch locations and enables efficient, targeted pruning.
  arXiv  Detail & Related papers  (2024-09-26T04:27:44Z)
• ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation [11.92011909884167]
  We introduce spatial cues by training a teacher network that leverages left-right image pairs as inputs. We apply both attention-adapted feature distillation and focal-depth-adapted response distillation in the training stage. Our experiments on the real depth estimation datasets KITTI and DrivingStereo demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2023-09-26T08:12:37Z)
• Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth Estimation [1.6775954077761863]
  We present a fully convolutional depth estimation network using contextual feature fusion. Compared to UNet++ and HRNet, we use high-resolution and low-resolution features to reserve information on small targets and fast-moving objects. Our method reduces the parameters without sacrificing accuracy.
  arXiv  Detail & Related papers  (2023-09-17T13:40:15Z)
• Lightweight Monocular Depth Estimation with an Edge Guided Network [34.03711454383413]
  We present a novel lightweight Edge Guided Depth Estimation Network (EGD-Net) In particular, we start out with a lightweight encoder-decoder architecture and embed an edge guidance branch. In order to aggregate the context information and edge attention features, we design a transformer-based feature aggregation module.
  arXiv  Detail & Related papers  (2022-09-29T14:45:47Z)
• SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning [53.78813049373321]
  We propose a self-supervised learning method for the pre-trained supervised monocular depth networks to enable metrically scaled depth estimation. Our approach is useful for various applications such as mobile robot navigation and is applicable to diverse environments.
  arXiv  Detail & Related papers  (2022-03-10T12:28:42Z)
• Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction [91.43066633305662]
  We propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD) Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time.
  arXiv  Detail & Related papers  (2022-03-09T17:20:18Z)
• Geometry Uncertainty Projection Network for Monocular 3D Object Detection [138.24798140338095]
  We propose a Geometry Uncertainty Projection Network (GUP Net) to tackle the error amplification problem at both inference and training stages. Specifically, a GUP module is proposed to obtains the geometry-guided uncertainty of the inferred depth. At the training stage, we propose a Hierarchical Task Learning strategy to reduce the instability caused by error amplification.
  arXiv  Detail & Related papers  (2021-07-29T06:59:07Z)
• Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion [56.85837052421469]
  Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from a single RGB image with optional sparse measurements from low-cost active depth sensors. We introduce Sparse Networks (SANs), a new module enabling monodepth networks to perform both the tasks of depth prediction and completion.
  arXiv  Detail & Related papers  (2021-03-30T21:22:26Z)
• On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation [1.9007546108571112]
  Inferring the depth of images is a fundamental inverse problem within the field of Computer Vision. We propose a new lightweight and fast supervised CNN architecture combined with novel feature extraction models. We also introduce an efficient surface normals module, jointly with a simple geometric 2.5D loss function, to solve SIDE problems.
  arXiv  Detail & Related papers  (2020-10-13T18:37:38Z)
• AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning [72.99415402575886]
  Outlier detection is an important data mining task with numerous practical applications. We propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model. Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance.
  arXiv  Detail & Related papers  (2020-06-19T18:57:51Z)
• Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread [136.2224792151324]
  We propose a new deep learning algorithm for fast salient object detection. The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
  arXiv  Detail & Related papers  (2020-01-22T15:23:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.