# YOLO-RS: Remote Sensing Enhanced Crop Detection Methods

arXiv: https://arxiv.org/abs/2504.11165 · HTML: https://mlsanity.com/paper/2504.11165/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 53 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 23 of 23)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [DSSD : Deconvolutional Single Shot Detector](https://mlsanity.com/paper/1701.06659/) (2017)
- [Single-Shot Refinement Neural Network for Object Detection](https://mlsanity.com/paper/1711.06897/) (2017)
- [M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network](https://mlsanity.com/paper/1811.04533/) (2018)
- [On the Integration of Self-Attention and Convolution](https://mlsanity.com/paper/2111.14556/) (2021)
- [Augmentation for small object detection](https://mlsanity.com/paper/1902.07296/) (2019)
- [Poly Kernel Inception Network for Remote Sensing Detection](https://mlsanity.com/paper/2403.06258/) (2024)
- [LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network](https://mlsanity.com/paper/2404.01614/) (2024)
- [Deep Feature Pyramid Reconfiguration for Object Detection](https://mlsanity.com/paper/1808.07993/) (2018)
- [Explainable Detection of Machine Generated Music and Early Systematic Evaluation](https://mlsanity.com/paper/2412.13421/) (2024)
- [PDT: Uav Target Detection Dataset for Pests and Diseases Tree](https://mlsanity.com/paper/2409.15679/) (2024)
- [M6: Multi-generator, Multi-domain, Multi-lingual and cultural, Multi-genres, Multi-instrument Machine-Generated Music Detection Databases](https://mlsanity.com/paper/2412.06001/) (2024)

## Built on by (most cited first, 1 of 1)

- [Attention-Based Ensemble Learning for Crop Classification Using Landsat 8-9 Fusion](https://mlsanity.com/paper/2506.18321/) (2025)

## Abstract (arXiv)

With the rapid development of remote sensing technology, crop classification and health detection based on deep learning have gradually become a research hotspot. However, the existing target detection methods show poor performance when dealing with small targets in remote sensing images, especially in the case of complex background and image mixing, which is difficult to meet the practical application requirementsite. To address this problem, a novel target detection model YOLO-RS is proposed in this paper. The model is based on the latest Yolov11 which significantly enhances the detection of small targets by introducing the Context Anchor Attention (CAA) mechanism and an efficient multi-field multi-scale feature fusion network. YOLO-RS adopts a bidirectional feature fusion strategy in the feature fusion process, which effectively enhances the model's performance in the detection of small targets. Small target detection. Meanwhile, the ACmix module at the end of the model backbone network solves the category imbalance problem by adaptively adjusting the contrast and sample mixing, thus enhancing the detection accuracy in complex scenes. In the experiments on the PDT remote sensing crop health detection dataset and the CWC crop classification dataset, YOLO-RS improves both the recall and the mean average precision (mAP) by about 2-3% or so compared with the existing state-of-the-art methods, while the F1-score is also significantly improved. Moreover, the computational complexity of the model only increases by about 5.2 GFLOPs, indicating its significant advantages in both performance and efficiency. The experimental results validate the effectiveness and application potential of YOLO-RS in the task of detecting small targets in remote sensing images.