This paper presents a lightweight and energy-efficient object detection solution for aerial imagery captured during emergency response situations. We focus on deploying the YOLOv4-Tiny model, a compact convolutional neural network, optimized through post-training quantization to INT8 precision. The model is trained on a custom-curated aerial emergency dataset, consisting of 10,820 annotated images covering critical emergency scenarios. Unlike prior works that rely on publicly available datasets, we created this dataset ourselves due to the lack of publicly available drone-view emergency imagery, making the dataset itself a key contribution of this work. The quantized model is evaluated against YOLOv5-small across multiple metrics, including mean Average Precision (mAP), F1 score, inference time, and model size. Experimental results demonstrate that the quantized YOLOv4-Tiny achieves comparable detection performance while reducing the model size from 22.5 MB to 6.4 MB and improving inference speed by 44\%. With a 71\% reduction in model size and a 44\% increase in inference speed, the quantized YOLOv4-Tiny model proves highly suitable for real-time emergency detection on low-power edge devices.
Enhancing deep learning performance on burned area delineation from SPOT-6/7 imagery for emergency management
IRISA, Université Bretagne Sud, UMR 6074, Vannes, France（IRISA，布列塔尼大学，UMR 6074，瓦纳兹，法国）；University of Salzburg, Department of Geoinformatics, Salzburg, Austria（萨尔茨堡大学，地理信息学院，萨尔茨堡，奥地利）；ICube-SERTIT, Université de Strasbourg, Illkirch Graffenstaden, France（ICube-SERTIT，斯特拉斯堡大学，伊尔克里希格拉芬施塔登，法国）
Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagery
Key Laboratory of Intelligent Computing & Signal Processing (Anhui University), Ministry of Education（智能计算与信号处理重点实验室（安徽大学））；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Artificial Intelligence, Anhui University（人工智能学院）；th Research Institute, China Electronics Technology Group Corporation（中国电子科技集团第三十八研究所）；School of Computer Science and Technology, Anhui University（计算机科学与技术学院）
New York University（纽约大学）；University of the Philippines Diliman（菲律宾大学迪里曼分校）；University of Washington（华盛顿大学）；German Corporation for International Cooperation GmbH (GIZ)（德国国际合作机构（GIZ））
Rapid damage assessment is of crucial importance to emergency responders during hurricane events, however, the evaluation process is often slow, labor-intensive, costly, and error-prone. New advances in computer vision and remote sensing open possibilities to observe the Earth at a different scale. However, substantial pre-processing work is still required in order to apply state-of-the-art methodology for emergency response. To enable the comparison of methods for automatic detection of damaged buildings from post-hurricane remote sensing imagery taken from both airborne and satellite sensors…
Skolkovo institute of Science and technology（斯科尔科沃科学技术研究院）；Moscow（莫斯科）
When major disaster occurs the questions are raised how to estimate the damage in time to support the decision making process and relief efforts by local authorities or humanitarian teams. In this paper we consider the use of Machine Learning and Computer Vision on remote sensing imagery to improve time efficiency of assessment of damaged buildings in disaster affected area. We propose a general workflow that can be useful in various disaster management applications, and demonstrate the use of the proposed workflow for the assessment of the damage caused by the wildfires in California in 2017.
Syed Babar Ali School of Science and Engineering（赛义德·巴巴尔·阿里科学与工程学院）；Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学（LUMS））
This paper proposes to employ a Inception-ResNet inspired deep learning architecture called Tiny-Inception-ResNet-v2 to eliminate bonded labor by identifying brick kilns within "Brick-Kiln-Belt" of South Asia. The framework is developed by training a network on the satellite imagery consisting of 11 different classes of South Asian region. The dataset developed during the process includes the geo-referenced images of brick kilns, houses, roads, tennis courts, farms, sparse trees, dense trees, orchards, parking lots, parks and barren lands. The dataset is made publicly available for further res…
Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression can cause severe decline in performance of deep Convolution Neural Network (CNN) architectures even when mild compression is applied and the resulting compressed imagery is visually identical. In this work, we apply the lossy JPEG compression method with six discrete levels of increasing compression {95, 75, 50, 15, 10, 5} to infrared band (thermal)…
We have developed a framework for crisis response and management that incorporates the latest technologies in computer vision (CV), inland flood prediction, damage assessment and data visualization. The framework uses data collected before, during, and after the crisis to enable rapid and informed decision making during all phases of disaster response. Our computer-vision model analyzes spaceborne and airborne imagery to detect relevant features during and after a natural disaster and creates metadata that is transformed into actionable information through web-accessible mapping tools. In part…
Semantic4Safety: Causal Insights from Zero-shot Street View Imagery Segmentation for Urban Road Safety
School of Geospatial Engineering and Science, Sun Yat-sen University（地理空间工程与科学学院，中山大学）；School of Geographical and Earth Sciences, University of Glasgow（地理与地球科学学院，格拉斯哥大学）；School of Economics and Management, Shanxi University（经济学与管理学院，山西大学）
Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a multitude of Computer Vision tasks. Such imagery typically contains objects from various categories, yet the multi-class interactive annotation setting for the detection task has thus far been unexplored. To address these needs, we propose a novel interactive annotation method for multiple instances of tiny objects from multiple classes, based on a few point-based user inputs. Our approach, C3Det, relates the full image context with annotator inputs in a local and global manner via late-fusion and featu…
A Multimodal Large Language Model-Driven Framework for Context-Aware UAV Emergency Landing Site Selection
School of Information Science and Engineering, Southeast University（信息科学与工程学院，东南大学）；LASER, International Digital Economy Academy（LASER，国际数字经济学院）；Department of Electronic Engineering, East China Normal University（电子工程系，华东师范大学）
Despite the breakthrough deep learning performances achieved for automatic object detection, small target detection is still a challenging problem, especially when looking at fast and accurate solutions suitable for mobile or edge applications. In this work we present YOLO-S, a simple, fast and efficient network for small target detection. The architecture exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation, and reshape-passthrough layer to alleviate the vanishing gradient problem, promote feature reuse across network and combine…
AIT Austrian Institute of Technology, Center for Digital Safety & Security（奥地利技术学院（AIT）数字安全与保障中心）；TU Wien, Institute of Visual Computing & Human-Centered Technology（维也纳技术大学视觉计算与以人类为中心技术研究所）
Vehicle detection in aerial and satellite images is still challenging due to their tiny appearance in pixels compared to the overall size of remote sensing imagery. Classical methods of object detection very often fail in this scenario due to violation of implicit assumptions made such as rich texture, small to moderate ratios between image size and object size. Satellite video is a very new modality which introduces temporal consistency as inductive bias. Approaches for vehicle detection in satellite video use either background subtraction, frame differencing or subspace methods showing moder…
COXNet: Cross-Layer Fusion with Adaptive Alignment and Scale Integration for RGBT Tiny Object Detection
Beijing Institute of Technology（北京理工大学）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；National Key Laboratory of Space Target Awareness, Space Engineering University（航天工程大学空间目标感知全国重点实验室）；Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China（教育部光电成像技术与系统重点实验室）；Chongqing Innovation Center, Beijing Institute of Technology（北京理工大学重庆创新中心）；China North Vehicle Research Institute（中国北方车辆研究所）
UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects
Easy Lab, College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China（易实验室，数学与信息学院，华南农业大学，广州，中国）；School of Computer Science and Engineering, Southeast University, Nanjing, China（计算机科学与工程学院，东南大学，南京，中国）；School of Mathematics, Foshan University, Foshan, China（数学学院，佛山大学，佛山，中国）
The University of New Mexico（新墨西哥大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
Intelligent detection and processing capabilities can be instrumental to improving the safety, efficiency, and successful completion of rescue missions conducted by firefighters in emergency first response settings. The objective of this research is to create an automated system that is capable of real-time, intelligent object detection and recognition and facilitates the improved situational awareness of firefighters during an emergency response. We have explored state of the art machine/deep learning techniques to achieve this objective. The goal for this work is to enhance the situational a…
A DeNoising FPN With Transformer R-CNN for Tiny Object Detection
Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University（电子工程系，阳明交通大学）；Institute of Electronics, National Yang Ming Chiao Tung University（电子研究所，阳明交通大学）；Department of Computer Science and Information Engineering, National Taiwan University (NTU)（计算机科学与信息工程系，国立台湾大学）
Now a days, UAVs such as drones are greatly used for various purposes like that of capturing and target detection from ariel imagery etc. Easy access of these small ariel vehicles to public can cause serious security threats. For instance, critical places may be monitored by spies blended in public using drones. Study in hand proposes an improved and efficient Deep Learning based autonomous system which can detect and track very small drones with great precision. The proposed system consists of a custom deep learning model Tiny YOLOv3, one of the flavors of very fast object detection model You…
A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level
Department of Ecology, Swedish University of Agricultural Sciences（生态系，瑞典农业科学大学）；University of Guelph（圭尔夫大学）；Vector Institute（向量研究所）；University of Helsinki（赫尔辛基大学）；Department of Entomology, National Museum of Natural History, Smithsonian Institution（昆虫学系，国家自然历史博物馆，史密松尼研究所）；Department of Biological and Environmental Science, University of Jyväskylä（生物与环境科学系，于韦斯屈埃大学）；Faculty of Biological and Environmental Sciences, University of Helsinki（生物与环境科学学院，赫尔辛基大学）；Centre for Biodiversity Genomics, University of Guelph（生物多样性基因组中心，圭尔夫大学）；NVIDIA（NVIDIA公司）；University of Toronto（多伦多大学）；Kilpisjärvi Biological Station, University of Helsinki（基利皮斯杰里生物学站，赫尔辛基大学）；Unit for Field-based Forest Research, Swedish University of Agricultural Sciences（基于现场的森林研究单位，瑞典农业科学大学）
Inception Institute of Artificial Intelligence, UAE（阿联酋Inception人工智能研究所）；Wuhan University, China（中国武汉大学）；Huazhong University of Science and Technology, China（中国华中科技大学）
Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. In this work, we introduce the first benchmark dataset for instance segmentation in aerial imagery that combines instance-level object detection and pixel-level segmentation tasks. In comparison to instance segmentation in natural scenes, aerial images present unique challenges e.g., a huge number of instances per image, large object-scale variations and abundant tiny objects. Our large-scale and densely annotated Instance Segmentation in Aerial Images Dataset (iSAID) comes with 655,451 object ins…
Multi-Modal Building Change Detection for Large-Scale Small Changes: Benchmark and Baseline
MOE Key Lab of ICSP, Anhui Provincial Key Lab of Multimodal Cognitive Computation, IMIS Lab of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei, China（教育部信息与通信领域重点实验室、安徽省多模态认知计算重点实验室、安徽省IMIS实验室、计算机科学与技术学院、安徽大学、合肥，中国）；School of Public Safety and Emergency Management, Anhui University of Science and Technology, Hefei 231131, China（公共安全与应急管理学院、安徽理工大学、合肥231131，中国）；College of Computing and Data Science, Nanyang Technological University, Singapore 639798（计算与数据科学学院、南洋理工大学、新加坡639798）；Shaanxi Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China（人工智能联合实验室、陕西科技大学、西安710021，中国）；School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China（软件工程学院、西安交通大学、西安710049，中国）；Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8561, Japan（前沿科学研究生院、东京大学、千叶，日本）
Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning
Built Environment Department, College of Science and Technology, North Carolina A&T State University（北卡罗来纳农工州立大学科技学院建筑环境系）；United Nations University Institute for Water, Environment and Health（联合国大学水、环境与健康研究所）
Benchmarking CNN and Transformer-Based Object Detectors for UAV Solar Panel Inspection
Faculty of Engineering, University of Moratuwa（摩图瓦大学工程学院）；STEM College, School of Computing Technologies, RMIT University（皇家墨尔本理工学院计算机技术学院）；School of Engineering, University of Southern Queensland（南方昆士兰大学工程学院）
Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications
Ozer Lab, Dept. of Computer Science, Ozyegin University（奥泽实验室，计算机科学系，奥兹根大学）；Dept. of Electrical and Computer Engineering, California State Polytechnic University, Pomona（电气与计算机工程系，加州州立理工大学庞纳分校）
German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich（慕尼黑工业大学）
Multi-pedestrian tracking in aerial imagery has several applications such as large-scale event monitoring, disaster management, search-and-rescue missions, and as input into predictive crowd dynamic models. Due to the challenges such as the large number and the tiny size of the pedestrians (e.g., 4 x 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e.g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well. In this paper, we propose AerialM…
RIS-LAD: A Benchmark and Model for Referring Low-Altitude Drone Image Segmentation
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China（教育部多媒体可信感知与高效计算重点实验室）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；School of Informatics, Xiamen University（厦门大学信息学院）
Engineering Vellore Institute of Technology, AP Guntur, India（工程维洛雷理工学院，AP古尔图，印度）；Engineering Vellore Institute of Technology, AP Ongole, India（工程维洛雷理工学院，AP翁戈尔，印度）；Engineering Vellore Institute of Technology, AP Vijayawada, India（工程维洛雷理工学院，AP维杰亚瓦达，印度）
