YOLO-SegNet: A Method for Individual Street Tree Segmentation Based on the Improved YOLOv8 and the SegFormer Network

Tingting Yang, Suyin Zhou, Aijun Xu (), Junhua Ye and Jianxin Yin
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Tingting Yang: College of Chemistry and Materials Engineering, Zhejiang Agriculture and Forestry University, Hangzhou 311800, China
Suyin Zhou: College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311800, China
Aijun Xu: College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311800, China
Junhua Ye: College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311800, China
Jianxin Yin: College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311800, China

Agriculture, 2024, vol. 14, issue 9, 1-19

Abstract: In urban forest management, individual street tree segmentation is a fundamental method to obtain tree phenotypes, which is especially critical. Most existing tree image segmentation models have been evaluated on smaller datasets and lack experimental verification on larger, publicly available datasets. Therefore, this paper, based on a large, publicly available urban street tree dataset, proposes YOLO-SegNet for individual street tree segmentation. In the first stage of the street tree object detection task, the BiFormer attention mechanism was introduced into the YOLOv8 network to increase the contextual information extraction and improve the ability of the network to detect multiscale and multishaped targets. In the second-stage street tree segmentation task, the SegFormer network was proposed to obtain street tree edge information more efficiently. The experimental results indicate that our proposed YOLO-SegNet method, which combines YOLOv8+BiFormer and SegFormer, achieved a 92.0% mean intersection over union (mIoU), 95.9% mean pixel accuracy (mPA), and 97.4% accuracy on a large, publicly available urban street tree dataset. Compared with those of the fully convolutional neural network (FCN), lite-reduced atrous spatial pyramid pooling (LR-ASPP), pyramid scene parsing network (PSPNet), UNet, DeepLabv3+, and HRNet, the mIoUs of our YOLO-SegNet increased by 10.5, 9.7, 5.0, 6.8, 4.5, and 2.7 percentage points, respectively. The proposed method can effectively support smart agroforestry development.

Keywords: urban street tree; individual tree segmentation; image instance segmentation; SegFormer; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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