RowDetr: End-to-End Row Detection Using Polynomials

• URL: http://arxiv.org/abs/2412.10525v2
• Date: Fri, 07 Mar 2025 00:00:57 GMT
• Title: RowDetr: End-to-End Row Detection Using Polynomials
• Authors: Rahul Harsha Cheppally, Ajay Sharda,
• Abstract summary: A novel end-to-end neural network architecture, RowDetr, is introduced for robust and efficient row detection.<n>A new dataset of approximately 6,900 images is curated, capturing a diverse range of real-world agricultural conditions.<n>A key innovation is PolyLoss, a novel energy-based loss function designed to enhance learning, even in the presence of noisy or imperfect labels.
• Score: 0.6278186810520364
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Crop row detection is essential for enabling autonomous navigation in GPS-denied environments, such as under-canopy agricultural settings. Traditional methods often struggle with occlusions, variable lighting conditions, and the structural variability of crop rows. To address these challenges, RowDetr, a novel end-to-end neural network architecture, is introduced for robust and efficient row detection. A new dataset of approximately 6,900 images is curated, capturing a diverse range of real-world agricultural conditions, including occluded rows, uneven terrain, and varying crop densities. Unlike previous approaches, RowDetr leverages smooth polynomial functions to precisely delineate crop boundaries in the image space, ensuring a more structured and interpretable representation of row geometry. A key innovation of this approach is PolyOptLoss, a novel energy-based loss function designed to enhance learning robustness, even in the presence of noisy or imperfect labels. This loss function significantly improves model stability and generalization by optimizing polynomial curve fitting directly in image space. Extensive experiments demonstrate that RowDetr significantly outperforms existing frameworks, including Agronav and RowColAttention, across key performance metrics. Additionally, RowDetr achieves a sixfold speedup over Agronav, making it highly suitable for real-time deployment on resource-constrained edge devices. To facilitate better comparisons across future studies, lane detection metrics from autonomous driving research are adapted, providing a more standardized and meaningful evaluation framework for crop row detection. This work establishes a new benchmark in under-canopy

Related papers

• RSAR: Restricted State Angle Resolver and Rotated SAR Benchmark [61.987291551925516]
  We introduce the Unit Cycle Resolver, which incorporates a unit circle constraint loss to improve angle prediction accuracy. Our approach can effectively improve the performance of existing state-of-the-art weakly supervised methods. With the aid of UCR, we further annotate and introduce RSAR, the largest multi-class rotated SAR object detection dataset to date.
  arXiv  Detail & Related papers  (2025-01-08T11:41:47Z)
• An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion for Long-tailed Multispectral Point Clouds [67.96583737413296]
  We propose an enhanced classification method based on adaptive multi-scale fusion for MPCs with long-tailed distributions.<n>In the training set generation stage, a grid-balanced sampling strategy is designed to reliably generate training samples from sparse labeled datasets.<n>In the feature learning stage, a multi-scale feature fusion module is proposed to fuse shallow features of land-covers at different scales.
  arXiv  Detail & Related papers  (2024-12-16T03:21:20Z)
• VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge [7.4646496981460855]
  This work presents a novel solution implementing a light autoregressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-09-23T08:46:15Z)
• PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection [59.355022416218624]
  integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. We propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN) PVAFN uses a multi-pooling strategy to integrate both multi-scale and region-specific information effectively.
  arXiv  Detail & Related papers  (2024-08-26T19:43:01Z)
• CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation [7.451629109566809]
  This paper introduces CurbNet, a novel framework for curb detection utilizing point cloud segmentation. We have developed the 3D-Curb dataset based on Semantic KITTI, currently the largest and most diverse collection of curb point clouds. To tackle the challenges posed by the uneven distribution of curb features on the xy-plane and their dependence on high-frequency features along the z-axis, we introduce the Multi-Scale and Channel Attention (MSCA) module.
  arXiv  Detail & Related papers  (2024-03-25T14:13:09Z)
• Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments [67.83787474506073]
  We tackle the limitations of current LiDAR-based 3D object detection systems. We introduce a universal textscFind n' Propagate approach for 3D OV tasks. We achieve up to a 3.97-fold increase in Average Precision (AP) for novel object classes.
  arXiv  Detail & Related papers  (2024-03-20T12:51:30Z)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• R2Det: Redemption from Range-view for Accurate 3D Object Detection [16.855672228478074]
  Redemption from Range-view Module (R2M) is a plug-and-play approach for 3D surface texture enhancement from the 2D range view to the 3D point view. R2M can be seamlessly integrated into state-of-the-art LiDAR-based 3D object detectors as preprocessing.
  arXiv  Detail & Related papers  (2023-07-21T10:36:05Z)
• 3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for Intelligent Transportation System [28.55894241049706]
  We propose a 3D harmonic loss function to relieve the pointcloud based inconsistent predictions. Our proposed method considerably improves the performance than benchmark models. Our code is open-source and publicly available.
  arXiv  Detail & Related papers  (2022-11-07T10:11:48Z)
• Vision based Crop Row Navigation under Varying Field Conditions in Arable Fields [6.088167023055281]
  We present a dataset for crop row detection with 11 field variations from Sugar Beet and Maize crops. We also present a novel crop row detection algorithm for visual servoing in crop row fields.
  arXiv  Detail & Related papers  (2022-09-28T11:23:34Z)
• Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots [10.386591972977207]
  This paper presents a robust crop row detection algorithm that withstands field variations using inexpensive cameras. A dataset of sugar beet images was created representing 11 field variations comprised of multiple grow stages, light levels, varying weed densities, curved crop rows and discontinuous crop rows. Our algorithm demonstrated robust vision-based crop row detection in challenging field conditions outperforming the baseline.
  arXiv  Detail & Related papers  (2022-09-09T12:47:24Z)
• Weakly Aligned Feature Fusion for Multimodal Object Detection [52.15436349488198]
  multimodal data often suffer from the position shift problem, i.e., the image pair is not strictly aligned. This problem makes it difficult to fuse multimodal features and puzzles the convolutional neural network (CNN) training. In this article, we propose a general multimodal detector named aligned region CNN (AR-CNN) to tackle the position shift problem.
  arXiv  Detail & Related papers  (2022-04-21T02:35:23Z)
• Stress-Testing LiDAR Registration [52.24383388306149]
  We propose a method for selecting balanced registration sets, which are challenging sets of frame-pairs from LiDAR datasets. Perhaps unexpectedly, we find that the fastest and simultaneously most accurate approach is a version of advanced RANSAC.
  arXiv  Detail & Related papers  (2022-04-16T05:10:55Z)
• Towards Infield Navigation: leveraging simulated data for crop row detection [6.088167023055281]
  We suggest the utilization of small real-world datasets along with additional data generated by simulations to yield similar crop row detection performance as that of a model trained with a large real world dataset. Our method could reach the performance of a deep learning based crop row detection model trained with real-world data by using 60% less labelled real-world data.
  arXiv  Detail & Related papers  (2022-04-04T19:28:30Z)
• Towards agricultural autonomy: crop row detection under varying field conditions using deep learning [4.252146169134215]
  This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection. A dataset with ten main categories encountered under various field conditions was used for testing. The effect on these conditions on the angular accuracy of crop row detection was compared.
  arXiv  Detail & Related papers  (2021-09-16T23:12:08Z)
• Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment [85.32080531133799]
  This paper reviews the classical problem of free-form curve registration and applies it to an efficient RGBD visual odometry system called Canny-VO. Two replacements for the distance transformation commonly used in edge registration are proposed: Approximate Nearest Neighbour Fields and Oriented Nearest Neighbour Fields. 3D2D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-12-15T11:42:17Z)
• Learning the Linear Quadratic Regulator from Nonlinear Observations [135.66883119468707]
  We introduce a new problem setting for continuous control called the LQR with Rich Observations, or RichLQR. In our setting, the environment is summarized by a low-dimensional continuous latent state with linear dynamics and quadratic costs. Our results constitute the first provable sample complexity guarantee for continuous control with an unknown nonlinearity in the system model and general function approximation.
  arXiv  Detail & Related papers  (2020-10-08T07:02:47Z)
• Reconfigurable Voxels: A New Representation for LiDAR-Based Point Clouds [76.52448276587707]
  We propose Reconfigurable Voxels, a new approach to constructing representations from 3D point clouds. Specifically, we devise a biased random walk scheme, which adaptively covers each neighborhood with a fixed number of voxels. We find that this approach effectively improves the stability of voxel features, especially for sparse regions.
  arXiv  Detail & Related papers  (2020-04-06T15:07:16Z)
• Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystr\"om method [76.73096213472897]
  We develop techniques which exploit spectral properties of the data matrix to obtain improved approximation guarantees. Our approach leads to significantly better bounds for datasets with known rates of singular value decay. We show that both our improved bounds and the multiple-descent curve can be observed on real datasets simply by varying the RBF parameter.
  arXiv  Detail & Related papers  (2020-02-21T00:43:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.