Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on deep learning and thus require vast amounts of training data. Due to their scarcity and minuscule size, annotating animals in aerial imagery is a highly tedious process. In this project, we present a methodology to reduce the amount of required training data by resorting to self-supervised pretraining. In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discrimination (CLD) to condition our model on the aerial images without the requirement for labels. We show that a combination of MoCo, CLD, and geometric augmentations outperforms conventional models pre-trained on ImageNet by a large margin. Crucially, our method still yields favorable results even if we reduce the number of training animals to just 10%, at which point our best model scores double the recall of the baseline at similar precision. This effectively allows reducing the number of required annotations to a fraction while still being able to train high-accuracy models in such highly challenging settings.
Microsoft AI for Good Lab（微软人工智能公益实验室）；Center for Research and Formation in Artificial Intelligence (Cinfonia), Universidad de los Andes（安第斯大学人工智能研究与培训中心）；Conservation X Labs（保护X实验室）；Kirk Larsen Consulting（Kirk Larsen咨询公司）；Tanzania Wildlife Research Institute（坦桑尼亚野生动物研究所）；Alaska Department of Fish and Game（阿拉斯加渔猎局）
Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on supervised learning and thus require vast amounts of training data. Due to their scarcity and minuscule size, annotating animals in aerial imagery is a highly tedious process. In this project, we present a methodology to reduce the amount of required training data by resorting to self-supervised pretraining. In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discriminat…
WildBox: A Dataset and Benchmark for Aerial Monocular 3D Detection of African Savanna Wildlife
D Optical Metrology Unit, Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会3D光学计量部）；Computer Vision and Machine Learning Systems group, Institute for Geoinformatics, University of Muenster（明斯特大学地理信息学研究所计算机视觉与机器学习系统组）；School of Civil, Aerospace and Design Engineering, University of Bristol（布里斯托大学土木、航空航天与设计工程学院）；Department of Biology, University of Southern Denmark（南丹麦大学生物学系）；The Ohio State University（俄亥俄州立大学）；Department of Collective Behavior, Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所集体行为系）；University of Konstanz（康斯坦茨大学）；Centre for the Advanced Study of Collective Behaviour, University of Konstanz（康斯坦茨大学集体行为高级研究中心）；Department of Biology, University of Konstanz（康斯坦茨大学生物学系）；Environmental Computational Science and Earth Observation Laboratory, EPFL（瑞士联邦理工学院环境计算科学与地球观测实验室）
Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dangerous and expensive, hence Unmanned Aerial Vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative tool to estimate livestock. Several works have been proposed that semi-automatically process UAV images to detect animals, of which some employ Convolutional Neural Networks (CNNs), a recent family of deep learning algorithms that proved very effective in object detection in large dataset…
Leveraging Self-Supervised Features for Efficient Flooded Region Identification in UAV Aerial Images
Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India（电子与通信工程系，Manipal理工学院，Manipal高等教育学院，Manipal，印度）
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；University of Zurich（苏黎世大学）；Wageningen University & Research（瓦赫宁根大学及研究中心）
Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub…
Transmission Line Defect Detection Based on UAV Patrol Images and Vision-language Pretraining
Department of Electronic and Communication Engineering, North China Electric Power University（电子与通信工程系，华北电力大学）；Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University（河北省电力物联网技术重点实验室，华北电力大学）；Computer Science and Software Engineering Department, Monmouth University（计算机科学与软件工程系，蒙特莫恩大学）
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；University of Otago（奥塔哥大学）；Princeton University（普林斯顿大学）；Marine Bycatch and Threats – Department of Conservation（海洋误捕与威胁——保护部门）；Antarctic Research Trust（南极研究信托）；The Tawaki Trust（塔瓦基信托）；Global Penguin Society（全球企鹅协会）
Weakly supervised framework for wildlife detection and counting in challenging Arctic environments: a case study on caribou (Rangifer tarandus)
Department of Applied Geomatics, Université de Sherbrooke（应用测绘系，谢伯罗克大学）；Quebec Centre for Biodiversity Science (QCBS), Stewart Biology, McGill University（魁北克生物多样性科学中心（QCBS）、斯图尔特生物学、麦吉尔大学）
Manipal Institute of Technology（马尼帕尔理工学院）；Manipal Academy of Higher Education（马尼帕尔高等教育学院）；Manipal Institute of Technology Bengaluru（马尼帕尔理工学院班加罗尔分校）
UAV based surveillance is gaining much interest worldwide due to its extensive applications in monitoring wildlife, urban planning, disaster management, campus security, etc. These videos are analyzed for strange/odd/anomalous patterns which are essential aspects of surveillance. But manual analysis of these videos is tedious and laborious. Hence, the development of computer-aided systems for the analysis of UAV based surveillance videos is crucial. Despite this interest, in literature, several computer aided systems are developed focusing only on CCTV based surveillance videos. These methods …
We present an Active Learning (AL) strategy for re-using a deep Convolutional Neural Network (CNN)-based object detector on a new dataset. This is of particular interest for wildlife conservation: given a set of images acquired with an Unmanned Aerial Vehicle (UAV) and manually labeled gound truth, our goal is to train an animal detector that can be re-used for repeated acquisitions, e.g. in follow-up years. Domain shifts between datasets typically prevent such a direct model application. We thus propose to bridge this gap using AL and introduce a new criterion called Transfer Sampling (TS). T…
Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches
Artificial Intelligence and Robotics Laboratory (LINAR)（人工智能与机器人实验室）；Universidad de San Andrés（圣安德烈大学）；Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)（国家科学与技术研究中心）；Centre for Automation and Robotics (CSIC-UPM)（自动化与机器人中心）；Museo Argentino Bernardino Rivadavia（阿根廷Bernardino Rivadavia博物馆）
BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes
Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；University of Konstanz（康斯坦茨大学）；Indian Institute of Science（印度科学学院）；BITS Pilani Hyderabad（BITS Pilani海得拉巴校区）
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
College of Computer and Information, Hohai University（河海大学计算机与信息学院）；Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；College of Information Science and Technology, Nanjing Forestry University（南京林业大学信息科学与技术学院）；Institute of Forest Resource Information Techniques, Chinese Academy of Forestry（中国林业科学研究院森林资源信息技术研究所）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
SugarViT -- Multi-objective Regression of UAV Images with Vision Transformers and Deep Label Distribution Learning Demonstrated on Disease Severity Prediction in Sugar Beet
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS（弗劳恩霍夫智能分析与信息系统研究所）；University of Bonn（波恩大学）；Institute for Sugar Beet Research (IfZ)（甜菜研究所）
