RowDetr-v2: End-to-End Crop Row Detection Using Polynomials

Introduction

RowDetr-v2 is an advanced implementation of the RowDetr framework for end-to-end crop row detection using polynomial representations. This repository provides the official code for the paper "RowDetr: End-to-End Crop Row Detection Using Polynomials", published in Smart Agricultural Technology (2025).

Crop row detection is essential for enabling autonomous robots to navigate in GPS-denied agricultural environments, particularly under the canopy where occlusions, gaps, and curved rows pose significant challenges to traditional vision-based methods. RowDetr-v2 addresses these limitations by leveraging attention mechanisms and polynomial-based modeling to achieve robust, post-processing-free detection.

Key features:

• End-to-end detection: No manual post-processing required.
• Polynomial parameterization: Represents crop rows as smooth polynomials for accurate curve fitting.
• Attention-based architecture: Utilizes transformer mechanisms for global context understanding.
• GPS-denied robustness: Optimized for under-canopy scenarios with heavy occlusions.

Installation

Prerequisites

• Python 3.11
• CUDA-enabled GPU (recommended for training/inference)
• Conda package manager

Setup

1. Clone the repository:

   git clone https://github.com/r4hul77/RowDetr-v2.git
   cd RowDetr-v2

2. Create and activate a Conda environment:

   conda create -n RowDetr python=3.11
   conda activate RowDetr

3. Install dependencies:

   pip install mmengine
   conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.1 -c pytorch -c nvidia
   conda install nvidia/label/cuda-12.1.0::cuda-toolkit
   pip install mmcv==2.2.0 -f https://download.openmmlab.com/mmcv/dist/cu121/torch2.4/index.html
   pip install timm
   pip install scipy
   pip install sortedcontainers
   pip install onnx
   pip install onnxscript
   pip install future tensorboard

4. (Optional) Install in development mode:

   pip install -e .

Dataset

The implementation uses the Crop Row Detection Dataset, specifically collected and labeled to overcome challenges in under-canopy environments. The dataset includes images and JSON annotations for train/test/validation splits.

Download

Download the dataset from Kaggle:

• Crop Row Detection Dataset

Structure

After downloading and extracting, the dataset follows this structure:

CropRowDetectionDataset/
├── Train/
│   ├── images/
│   │   ├── image1.jpg
│   │   └── ...
│   └── labels/
│       ├── image1.json
│       └── ...
├── Test/
│   ├── images/
│   └── labels/
└── Validation/
    ├── images/
    └── labels/

JSON Annotation Format

Each JSON file contains:

• img_id: Image identifier.
• labels: List of crop rows with name, x, y coordinates, and alpha (normalized distance values for polynomial parameterization).

Example:

{
  "img_id": 0,
  "labels": [
    {
      "name": "row_0",
      "x": [530.843, 588.246, ...],
      "y": [1189.066, 1044.191, ...],
      "alpha": [0.0, 0.15029798560170535, ...]
    }
  ]
}

For detailed dataset description, see the dataset README or the Kaggle page.

Usage

Training

TODO

Evaluation

TODO

Inference

TODO

For more details, see docs/USAGE.md.

Model Architecture

RowDetr builds on DETR-like transformer architectures but specializes in:

• Polynomial Head: Outputs coefficients for row polynomials instead of bounding boxes.
• Alpha Normalization: Uses normalized distances (alpha) for consistent row parameterization.
• Multi-Row Detection: Handles multiple parallel crop rows in a single image.

Results

The RowDetr-v2 models, along with comparative baselines, achieve state-of-the-art performance on the Crop Row Detection Dataset. The table below summarizes the results:

Model	Latency (↓)	Param Count (↓)	LPD (↓)	TuSimple F1 (↑)	TuSimple FPR (↓)	TuSimple FNR (↓)
RowDetr[efficientnet]	9.11 ms	23M	0.405	0.734	0.393	0.044
RowDetr[resnet18]	6.7 ms	31M	0.421	0.736	0.391	0.043
RowDetr[regnetx_008]	9.7 ms	27M	0.416	0.725	0.404	0.046
RowDetr[resnet50]	9.25 ms	44M	0.413	0.740	0.384	0.046
Agronav	18 ms	NA	0.825	NA	NA	NA
RowCol [12]	14.16 ms	35M	1.48	0.3191	0.8028	0.0400

• Latency: Inference time per image (lower is better).
• Param Count: Number of model parameters (lower is better).
• LPD: Lane Position Deviation (lower is better).
• TuSimple F1: F1 score on TuSimple dataset (higher is better).
• TuSimple FPR: False Positive Rate on TuSimple dataset (lower is better).
• TuSimple FNR: False Negative Rate on TuSimple dataset (lower is better).
• Results reported on Test set with respective backbones.

Citation

If you use RowDetr or the dataset in your research, please cite:

@article{CHEPPALLY2025101494,
  title = {RowDetr: End-to-End Crop Row Detection Using Polynomials},
  author = {Rahul Harsha Cheppally and Ajay Sharda},
  journal = {Smart Agricultural Technology},
  pages = {101494},
  year = {2025},
  issn = {2772-3755},
  doi = {10.1016/j.atech.2025.101494},
  url = {https://www.sciencedirect.com/science/article/pii/S2772375525007257},
  keywords = {Crop row detection, Autonomous navigation, Agricultural Robotics, Attention mechanism}
}

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/AmazingFeature).
3. Commit changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

Acknowledgments

• Thanks to the DETR team for the foundational architecture.
• The dataset collection was supported by NSF-NRI.
• Special thanks to contributors and early testers.

License

This work is published under Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND)

Contact

For questions or issues:

• Open an issue on GitHub.

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Last updated: Jan 13, 2026 at 12:41 PM CDT