Advancements in machine learning, computer vision, and robotics have paved the way for transformative solutions in various domains, particularly in agriculture. For example, accurate identification and segmentation of fruits from field images plays a crucial role in automating jobs such as harvesting, disease detection, and yield estimation. However, achieving robust and precise infield fruit segmentation remains a challenging task since large amounts of labeled data are required to handle variations in fruit size, shape, color, and occlusion.
This repository provides source code for our 2024 ICRA paper titled "Few-Shot Fruit Segmentation via Transfer Learning." Our light-weight three-branch decoder effectively learns task specific boundaries enabling knowledge transfer of learned shapes and boundaries between tasks. By leveraging pre-trained neural networks, we demonstrate that accurate semantic segmentation of fruit in the field can be achieved with only a few labeled images. In addition, we show that models with pre-training learn to distinguish between fruit still on the trees and fruit that have fallen on the ground, and they can effectively transfer the knowledge to the target fruit dataset.
If you find this project useful, then please consider citing our work.
@inproceedings{james2024few,
title={Few-Shot Fruit Segmentation via Transfer Learning},
author={James, Jordan A and Manching, Heather K and Hulse-Kemp, Amanda M and Beksi, William J},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
pages={},
year={2024}
}
First, begin by cloning the project
$ git clone https://github.com/robotic-vision-lab/Few-Shot-Fruit-Segmentation-Via-Transfer-Learning.git
$ cd Few-Shot-Fruit-Segmentation-Via-Transfer-Learning
Then, set up a Python 3 environment by installing Pytorch (1.0.1 or higher) and TorchVision. Finally, install the remaining packages:
$ pip install -r requirements.txt
Scripts for training, evaluating, and visualizing the network are provided in the tools folder.
The evaluation results on the MinneApple test set are shown below.
| Pre-Training | Training Images | Test mIoU (%) | Test Accuracy (%) |
|---|---|---|---|
| CitDet | 0 | 67.2 | 97.3 |
| None | 2 | 64.3 | 96.1 |
| ImageNet | 2 | 66.7 | 95.8 |
| CitDet | 2 | 76.7 | 98.0 |
| None | 4 | 67.9 | 96.1 |
| ImageNet | 4 | 69.5 | 96.9 |
| CitDet | 4 | 75.1 | 98.1 |
| None | 670 | 78.6 | 98.3 |
| ImageNet | 670 | 82.1 | 98.7 |
| CitDet | 670 | 82.5 | 98.7 |
Our implementation is based on PIDNet and HRNet-Semantic-Segmentation.
