Visual inspections of bridges are critical to ensure their safety and identify potential failures early. This inspection process can be rapidly and accurately automated by using unmanned aerial vehicles (UAVs) integrated with deep learning models. However, choosing an appropriate model that is lightweight enough to integrate into the UAV and fulfills the strict requirements for inference time and accuracy is challenging. Therefore, our work contributes to the advancement of this model selection process by conducting a benchmark of 23 models belonging to the four newest YOLO variants (YOLOv5, YOLOv6, YOLOv7, YOLOv8) on COCO-Bridge-2021+, a dataset for bridge details detection. Through comprehensive benchmarking, we identify YOLOv8n, YOLOv7tiny, YOLOv6m, and YOLOv6m6 as the models offering an optimal balance between accuracy and processing speed, with mAP@50 scores of 0.803, 0.837, 0.853, and 0.872, and inference times of 5.3ms, 7.5ms, 14.06ms, and 39.33ms, respectively. Our findings accelerate the model selection process for UAVs, enabling more efficient and reliable bridge inspections.
Small Dents, Big Impact: A Dataset and Deep Learning Approach for Vehicle Dent Detection
School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST)（电气工程与计算机科学学院 国家科学与技术大学）；Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano（电子信息与生物工程系 米兰理工学院）
Robust Lightweight Crack Classification for Real-Time UAV Bridge Inspection
Bay Area Super Bridge Maintenance Technology Center, Guangdong Provincial Highway Construction Co., Ltd., Guangdong, China（湾区超级桥梁维护技术中心、广东省高速公路建设有限公司、广东，中国）；Guangdong AIHISUN Technology Co., Ltd., Guangdong, China（广东AIHISUN技术有限公司、广东，中国）
Silesian University of Technology（西里西亚工业大学）；Polish Academy of Sciences（波兰科学院）
The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem of detecting damage after sudden events by using an interconnected set of deep machine learning models organized in a single pipeline and allowing for easy modification and swapping models seamlessly. Models in the pipeline were trained with a synthetic dataset and were adapted to be further evalua…
University of Zagreb, Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算学院）
In this paper we propose a novel approach to generate a synthetic aerial dataset for application in UAV monitoring. We propose to accentuate shape-based object representation by applying texture randomization. A diverse dataset with photorealism in all parameters such as shape, pose, lighting, scale, viewpoint, etc. except for atypical textures is created in a 3D modelling software Blender. Our approach specifically targets two conditions in aerial images where texture of objects is difficult to detect, namely challenging illumination and objects occupying only a small portion of the image. Ex…
Forschungszentrum Jülich GmbH（于利希研究中心）；Helmholtz-Institute Erlangen-Nuremberg for Renewable Energies (HI ERN)（埃尔朗根-纽伦堡亥姆霍兹可再生能源研究所）；Institute Materials for Electronics and Energy Technology, Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡大学电子与能源技术材料研究所）
Increasing deployment of photovoltaics (PV) plants demands for cheap and fast inspection. A viable tool for this task is thermographic imaging by unmanned aerial vehicles (UAV). In this work, we develop a computer vision tool for the semi-automatic extraction of PV modules from thermographic UAV videos. We use it to curate a dataset containing 4.3 million IR images of 107842 PV modules from thermographic videos of seven different PV plants. To demonstrate its use for automated PV plant inspection, we train a ResNet-50 to classify ten common module anomalies with more than 90 % test accuracy. E…
Beyond Balanced Accuracy: A Resolution and Parity-Controlled Benchmark for Vision-Language and Vision-Only Defect Assessment in UAV Power-Line Inspection
State Grid Sichuan Electric Power Research Institute（国网四川省电力公司电力科学研究院）；Power System Security and Operation Key Laboratory of Sichuan Province（四川省电力系统安全与运行重点实验室）
CF-YOLO: Context-Aware Feature Refinement for Camouflaged Industrial Micro-Defect Detection
School of Information Engineering, Huzhou University（湖州大学信息工程学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
ETH Zurich（苏黎世联邦理工学院）；Mantis Technologies（曼蒂斯科技公司）
In this paper, we present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track, localize, and re-identify humans from UAVs flying at high altitude. In each spectrum, a customized RetinaNet network with ResNet backbone provides human detections which are subsequently fused to minimize the overall false detection rate. We show that by optimizing the bounding box anchors and augmenting the image resolution the number of missed detections from high altitudes can be decreased by over 20 percent. Our proposed network is compare…
Drones or unmanned aerial vehicles are traditionally used for military missions, warfare, and espionage. However, the usage of drones has significantly increased due to multiple industrial applications involving security and inspection, transportation, research purposes, and recreational drone flying. Such an increased volume of drone activity in public spaces requires regulatory actions for purposes of privacy protection and safety. Hence, detection of illegal drone activities such as boundary encroachment becomes a necessity. Such detection tasks are usually automated and performed by deep l…
COMSATS University Islamabad（COMSATS伊斯兰堡大学）；Zhejiang University（浙江大学）
Technological advancements have normalized the usage of unmanned aerial vehicles (UAVs) in every sector, spanning from military to commercial but they also pose serious security concerns due to their enhanced functionalities and easy access to private and highly secured areas. Several instances related to UAVs have raised security concerns, leading to UAV detection research studies. Visual techniques are widely adopted for UAV detection, but they perform poorly at night, in complex backgrounds, and in adverse weather conditions. Therefore, a robust night vision-based drone detection system is …
Detecting and Classifying Defective Products in Images Using YOLO
Northeastern University（东北大学）；Florida International University（佛罗里达国际大学）；University of California San Diego（加州大学圣地亚哥分校）；Tulane University（杜兰大学）；Kanazawa University（金泽大学）；Northern Arizona University（北亚利桑那大学）
The usage of Unmanned Aerial Vehicles (UAVs) in the context of structural health inspection is recently gaining tremendous popularity. Camera mounted UAVs enable the fast acquisition of a large number of images often used for mapping, 3D model reconstruction, and as an assisting tool for inspectors. Due to the number of images captured during large scale UAV surveys, a manual image-based inspection analysis of entire assets cannot be efficiently performed by qualified engineers. Additionally, comparing defects to past inspections requires the retrieval of relevant images which is often impract…
Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications. However, real-time scene parsing through object detection running on a UAV platform is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, in this paper we propose to learn efficient deep object detectors through channel pruning of convolutional layers. To this end, we enforce channel-level sparsity of convolutional layers by imposing L1 regularization o…
PDT: Uav Target Detection Dataset for Pests and Diseases Tree
Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东省计算中心（国家超级计算济南中心））；Shandong Provincial Key Laboratory of Computer Networks（山东省计算机网络重点实验室）；Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心）；SHANDONG SCICOM Information and Economy Research Institute Co., Ltd.（山东科信信息经济研究院有限公司）；Peking University（北京大学）
A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images
Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florianópolis, Brazil（自动化与系统工程系，圣卡塔琳娜联邦大学，巴西弗洛里安波利斯）；Applications Lab, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain（应用实验室，科学学院，萨拉曼卡大学，西班牙萨拉曼卡）
UAVs have been widely used in visual inspections of buildings, bridges and other structures. In either outdoor autonomous or semi-autonomous flights missions strong GPS signal is vital for UAV to locate its own positions. However, strong GPS signal is not always available, and it can degrade or fully loss underneath large structures or close to power lines, which can cause serious control issues or even UAV crashes. Such limitations highly restricted the applications of UAV as a routine inspection tool in various domains. In this paper a vision-model-based real-time self-positioning method is …
The Chinese University of Hong Kong（香港中文大学）；Minjiang University（闽江学院）
Automatic crack detection and segmentation play a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG, and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and its performance of it is compared with other state-of-the-art network architecture. We have summarized the existin…
Improved YOLOv5s model for key components detection of power transmission lines
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Electric Power Research Institute, Yunnan Power Grid Co., Ltd（云南电网有限责任公司电力科学研究院）
UAVs showed great efficiency on scanning bridge decks surface by taking a single shot or through stitching a couple of overlaid still images. If potential surface deficits are identified through aerial images, subsequent ground inspections can be scheduled. This two-phase inspection procedure showed great potentials on increasing field inspection productivity. Since aerial and ground inspection images are taken at different scales, a tool to properly fuse these multi-scale images is needed for improving the current bridge deck condition monitoring practice. In response to this need a data fusi…
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）
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；University of Petroleum and Energy Studies（石油与能源研究大学）
Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. The vulnerability of a building to earthquake can be assessed through inspection that takes into account the expected damage progression of the associated component and the component's contribution to structural system performance. Most of these inspections are done manually, leading to high utilization of manpower, time, and cost. This paper proposes a methodology to auto…
Vietnam National University（越南国立大学）；University of Technology Sydney（悉尼科技大学）
In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to…
Bridges are an essential part of the transportation infrastructure and need to be monitored periodically. Visual inspections by dedicated teams have been one of the primary tools in structural health monitoring (SHM) of bridge structures. However, such conventional methods have certain shortcomings. Manual inspections may be challenging in harsh environments and are commonly biased in nature. In the last decade, camera-equipped unmanned aerial vehicles (UAVs) have been widely used for visual inspections; however, the task of automatically extracting useful information from raw images is still …
Henan University of Urban Construction（河南城建学院）；University of North Carolina（北卡罗来纳大学）；Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences（中国科学院东北地理与农业生态研究所）；Zhengzhou University（郑州大学）
The use of ground control points (GCPs) for georeferencing is the most common strategy in unmanned aerial vehicle (UAV) photogrammetry, but at the same time their collection represents the most time-consuming and expensive part of UAV campaigns. Recently, deep learning has been rapidly developed in the field of small object detection. In this letter, to automatically extract coordinates information of ground control points (GCPs) by detecting GCP-markers in UAV images, we propose a solution that uses a deep learning-based architecture, YOLOv5-OBB, combined with a confidence threshold filtering…
Small object detection has major applications in the fields of UAVs, surveillance, farming and many others. In this work we investigate the performance of state of the art Yolo based object detection models for the task of small object detection as they are one of the most popular and easy to use object detection models. We evaluated YOLOv5 and YOLOX models in this study. We also investigate the effects of slicing aided inference and fine-tuning the model for slicing aided inference. We used the VisDrone2019Det dataset for training and evaluating our models. This dataset is challenging in the …
Resource-Constrained UAV-Based Weed Detection for Site-Specific Management on Edge Devices
Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS, USA（农业与生物工程系，密苏里州立大学）；Sustainable Agriculture Research Unit, Mississippi State, MS, USA（可持续农业研究单位，密苏里州）；Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS, USA（植物与土壤科学系，密苏里州立大学）；Biosystems Engineering, North Dakota State University, Fargo, ND, USA（生物系统工程系，北达科塔州立大学）；NDSU Peltier Institute for the Advancement of Agricultural Technology, North Dakota State University, Fargo, ND, USA（NDSU裴特里农业技术进步研究所，北达科塔州立大学）
