Camera-Pose Robust Crater Detection from Chang'e 5

• URL: http://arxiv.org/abs/2406.04569v2
• Date: Fri, 12 Jul 2024 09:09:39 GMT
• Title: Camera-Pose Robust Crater Detection from Chang'e 5
• Authors: Matthew Rodda, Sofia McLeod, Ky Cuong Pham, Tat-Jun Chin,
• Abstract summary: We evaluate the performance of Mask R-CNN for crater detection, comparing models pretrained on simulated data containing off-nadir view angles and to pretraining on real-lunar images. We demonstrate pretraining on real-lunar images is superior despite the lack of images containing off-nadir view angles, achieving detection performance of 63.1 F1-score and ellipse-regression performance of 0.701 intersection over union.
• Score: 18.986915927640396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As space missions aim to explore increasingly hazardous terrain, accurate and timely position estimates are required to ensure safe navigation. Vision-based navigation achieves this goal through correlating impact craters visible through onboard imagery with a known database to estimate a craft's pose. However, existing literature has not sufficiently evaluated crater-detection algorithm (CDA) performance from imagery containing off-nadir view angles. In this work, we evaluate the performance of Mask R-CNN for crater detection, comparing models pretrained on simulated data containing off-nadir view angles and to pretraining on real-lunar images. We demonstrate pretraining on real-lunar images is superior despite the lack of images containing off-nadir view angles, achieving detection performance of 63.1 F1-score and ellipse-regression performance of 0.701 intersection over union. This work provides the first quantitative analysis of performance of CDAs on images containing off-nadir view angles. Towards the development of increasingly robust CDAs, we additionally provide the first annotated CDA dataset with off-nadir view angles from the Chang'e 5 Landing Camera.

Related papers

• AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis [57.249817395828174]
  We propose a scalable framework combining pseudo-synthetic renderings from 3D city-wide meshes with real, ground-level crowd-sourced images. The pseudo-synthetic data simulates a wide range of aerial viewpoints, while the real, crowd-sourced images help improve visual fidelity for ground-level images. Using this hybrid dataset, we fine-tune several state-of-the-art algorithms and achieve significant improvements on real-world, zero-shot aerial-ground tasks.
  arXiv  Detail & Related papers  (2025-04-17T17:57:05Z)
• Finding the Reflection Point: Unpadding Images to Remove Data Augmentation Artifacts in Large Open Source Image Datasets for Machine Learning [0.0]
  We propose a systematic algorithm to delineate the reflection boundary through a minimum mean squared error approach. Our method effectively identifies the transition between authentic content and its mirrored counterpart, even in the presence of compression or noise.
  arXiv  Detail & Related papers  (2025-04-04T04:54:10Z)
• 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)
• RaCFormer: Towards High-Quality 3D Object Detection via Query-based Radar-Camera Fusion [58.77329237533034]
  We propose a Radar-Camera fusion transformer (RaCFormer) to boost the accuracy of 3D object detection. RaCFormer achieves superior results of 64.9% mAP and 70.2% on nuScenes datasets.
  arXiv  Detail & Related papers  (2024-12-17T09:47:48Z)
• XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis [84.23233209017192]
  This paper presents a novel driving view synthesis dataset and benchmark specifically designed for autonomous driving simulations. The dataset is unique as it includes testing images captured by deviating from the training trajectory by 1-4 meters. We establish the first realistic benchmark for evaluating existing NVS approaches under front-only and multi-camera settings.
  arXiv  Detail & Related papers  (2024-06-26T14:00:21Z)
• FlightScope: A Deep Comprehensive Review of Aircraft Detection Algorithms in Satellite Imagery [2.9687381456164004]
  This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. This research encompasses an array of methodologies including YOLO versions 5 and 8, Faster RCNN, CenterNet, RetinaNet, RTMDet, and DETR, all trained from scratch. YOLOv5 emerges as a robust solution for aerial object detection, underlining its importance through superior mean average precision, Recall, and Intersection over Union scores.
  arXiv  Detail & Related papers  (2024-04-03T17:24:27Z)
• StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS [106.62225866064313]
  We present StereoPose, a novel stereo image framework for category-level object pose estimation. For a robust estimation from pure stereo images, we develop a pipeline that decouples category-level pose estimation into object size estimation, initial pose estimation, and pose refinement. To address the issue of image content aliasing, we define a back-view NOCS map for the transparent object. The back-view NOCS aims to reduce the network learning ambiguity caused by content aliasing, and leverage informative cues on the back of the transparent object for more accurate pose estimation.
  arXiv  Detail & Related papers  (2022-11-03T08:36:09Z)
• A Multi-purpose Real Haze Benchmark with Quantifiable Haze Levels and Ground Truth [61.90504318229845]
  This paper introduces the first paired real image benchmark dataset with hazy and haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene. A subset of this dataset has been used for the Object Detection in Haze Track of CVPR UG2 2022 challenge.
  arXiv  Detail & Related papers  (2022-06-13T19:14:06Z)
• A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose Prediction [0.0]
  Current software functions optimally only on near-nadir images, though off-nadir images are often the first sources of information following a natural disaster. This study proposes a deep learning ensemble framework to predict geocentric pose using 5,923 near-nadir and off-nadir RGB satellite images of cities worldwide.
  arXiv  Detail & Related papers  (2022-05-04T08:33:41Z)
• Improving Building Segmentation for Off-Nadir Satellite Imagery [16.747041713724066]
  Building segmentation is an important task for satellite imagery analysis and scene understanding. We propose a method that is able to provide accurate building segmentation for satellite imagery captured from a large range of off-nadir angles.
  arXiv  Detail & Related papers  (2021-09-08T22:55:16Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges [124.48654341780431]
  We present a large-scale dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. We build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated.
  arXiv  Detail & Related papers  (2021-02-24T11:20:55Z)
• Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation [16.99302954185652]
  It remains an open challenge to train deep convolutional neural networks (DCNNs) using only a small quantity of training samples. This paper explores an effective training data augmentation approach that can be employed to improve the overall DCNN performance.
  arXiv  Detail & Related papers  (2020-12-14T19:14:35Z)
• Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles [4.555256739812733]
  This work describes a process to estimate a precise vehicle position from aerial imagery. The state-of-the-art deep neural network Mask-RCNN is applied for that purpose. A mean accuracy of 20 cm can be achieved with flight altitudes up to 100 m, Full-HD resolution and a frame-by-frame detection.
  arXiv  Detail & Related papers  (2020-04-17T12:29:40Z)
• Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection [63.942632088208505]
  We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects.
  arXiv  Detail & Related papers  (2020-03-28T18:51:43Z)

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.