# Tree Detection and Diameter Estimation Based on Deep Learning

arXiv: https://arxiv.org/abs/2210.17424 · HTML: https://mlsanity.com/paper/2210.17424/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 46 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Virtual KITTI 2](https://mlsanity.com/paper/2001.10773/) (2020)
- [Virtual Worlds as Proxy for Multi-Object Tracking Analysis](https://mlsanity.com/paper/1605.06457/) (2016)
- [Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks](https://mlsanity.com/paper/1812.07252/) (2018)
- [TransPose: Keypoint Localization via Transformer](https://mlsanity.com/paper/2012.14214/) (2020)
- [Pointly-Supervised Instance Segmentation](https://mlsanity.com/paper/2104.06404/) (2021)
- [CBNet: A Composite Backbone Network Architecture for Object Detection](https://mlsanity.com/paper/2107.00420/) (2021)
- [Robust Object Detection under Occlusion with Context-Aware CompositionalNets](https://mlsanity.com/paper/2005.11643/) (2020)
- [On Pre-Trained Image Features and Synthetic Images for Deep Learning](https://mlsanity.com/paper/1710.10710/) (2017)
- [From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence](https://mlsanity.com/paper/2110.15245/) (2021)
- [Towards Autonomous Robotic Precision Harvesting: Mapping, Localization, Planning and Control for a Legged Tree Harvester](https://mlsanity.com/paper/2104.10110/) (2021)

## Built on by (most cited first, 12 of 12)

- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [AI applications in forest monitoring need remote sensing benchmark datasets](https://mlsanity.com/paper/2212.09937/) (2022)
- [FinnWoodlands Dataset](https://mlsanity.com/paper/2304.00793/) (2023)
- [TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations](https://mlsanity.com/paper/2501.07360/) (2025)
- [Keypoint Semantic Integration for Improved Feature Matching in Outdoor Agricultural Environments](https://mlsanity.com/paper/2503.08843/) (2025)
- [Survey on Datasets for Perception in Unstructured Outdoor Environments](https://mlsanity.com/paper/2404.18750/) (2024)
- [Scaling Up Forest Vision with Synthetic Data](https://mlsanity.com/paper/2509.11201/) (2025)
- [Stochastic stem bucking using mixture density neural networks](https://mlsanity.com/paper/2407.00510/) (2024)
- [WaveInst: A Frequency-Domain Enhanced Network for Fine-Grained Thin Tree Trunk Extraction in Forest Scenes](https://mlsanity.com/paper/2505.01656/) (2025)
- [Miti360: A Comprehensive Dataset for Improved Reforestation Monitoring](https://mlsanity.com/paper/2606.29447/) (2026)
- [A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa](https://mlsanity.com/paper/2608.11053/) (2026)
- [ProGuT: Label-Efficient Panoptic Segmentation for Forest Scenes](https://mlsanity.com/paper/2609.36891/) (2026)

## Abstract (arXiv)

Tree perception is an essential building block toward autonomous forestry operations. Current developments generally consider input data from lidar sensors to solve forest navigation, tree detection and diameter estimation problems. Whereas cameras paired with deep learning algorithms usually address species classification or forest anomaly detection. In either of these cases, data unavailability and forest diversity restrain deep learning developments for autonomous systems. So, we propose two densely annotated image datasets - 43k synthetic, 100 real - for bounding box, segmentation mask and keypoint detections to assess the potential of vision-based methods. Deep neural network models trained on our datasets achieve a precision of 90.4% for tree detection, 87.2% for tree segmentation, and centimeter accurate keypoint estimations. We measure our models' generalizability when testing it on other forest datasets, and their scalability with different dataset sizes and architectural improvements. Overall, the experimental results offer promising avenues toward autonomous tree felling operations and other applied forestry problems. The datasets and pre-trained models in this article are publicly available on hrefhttps://github.com/norlab-ulaval/PercepTreeV1GitHub (https://github.com/norlab-ulaval/PercepTreeV1).