Drones Pub Date : 2024-07-19 DOI: 10.3390/drones8070333

Zhiwei Jia, Yongkang Ouyang, Chao Feng, Shaosheng Fan, Zheng Liu, Chenhao Sun

{"title":"A Live Detecting System for Strain Clamps of Transmission Lines Based on Dual UAVs’ Cooperation","authors":"Zhiwei Jia, Yongkang Ouyang, Chao Feng, Shaosheng Fan, Zheng Liu, Chenhao Sun","doi":"10.3390/drones8070333","DOIUrl":"https://doi.org/10.3390/drones8070333","url":null,"abstract":"Strain clamps are critical components in high-voltage overhead transmission lines, and detection of their defects becomes an important part of regular inspection of transmission lines. A dual UAV (unmanned aerial vehicle) system was proposed to detect strain clamps in multiple split-phase conductors. The main UAV was equipped with a digital radiography (DR) imaging device, a mechanical arm, and an edge intelligence module with visual sensors. The slave UAV was equipped with a digital imaging board and visual sensors. A workflow was proposed for this dual UAV system. Target detection and distance detection of the strain clamps, as well as detection of the defects of strain clamps in DR images, are the main procedures of this workflow. To satisfy the demands of UAV-borne and real-time deployment, the improved YOLOv8-TR algorithm was proposed for the detection of strain clamps (the mAP@50 was 60.9%), and the KD-ResRPA algorithm is used for detecting defects in DR images (the average AUCROC of the three datasets was 82.7%). Field experiments validated the suitability of our dual UAV-based system for charged detection of strain clamps in double split-phase conductors, demonstrating its potential for practical application in live detecting systems.","PeriodicalId":507567,"journal":{"name":"Drones","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vision-Based Anti-UAV Detection Based on YOLOv7-GS in Complex Backgrounds 复杂背景下基于 YOLOv7-GS 的反无人机视觉检测

Drones Pub Date : 2024-07-18 DOI: 10.3390/drones8070331

Chunjuan Bo, Yuntao Wei, Xiujia Wang, Zhan Shi, Ying Xiao

引用次数: 0

YOMO-Runwaynet: A Lightweight Fixed-Wing Aircraft Runway Detection Algorithm Combining YOLO and MobileRunwaynet YOMO-Runwaynet：结合 YOLO 和 MobileRunwaynet 的轻量级固定翼飞机跑道检测算法

Drones Pub Date : 2024-07-18 DOI: 10.3390/drones8070330

Wei Dai, Zhengjun Zhai, Dezhong Wang, Zhaozi Zu, Siyuan Shen, Xinlei Lv, Sheng Lu, Lei Wang

{"title":"YOMO-Runwaynet: A Lightweight Fixed-Wing Aircraft Runway Detection Algorithm Combining YOLO and MobileRunwaynet","authors":"Wei Dai, Zhengjun Zhai, Dezhong Wang, Zhaozi Zu, Siyuan Shen, Xinlei Lv, Sheng Lu, Lei Wang","doi":"10.3390/drones8070330","DOIUrl":"https://doi.org/10.3390/drones8070330","url":null,"abstract":"The runway detection algorithm for fixed-wing aircraft is a hot topic in the field of aircraft visual navigation. High accuracy, high fault tolerance, and lightweight design are the core requirements in the domain of runway feature detection. This paper aims to address these needs by proposing a lightweight runway feature detection algorithm named YOMO-Runwaynet, designed for edge devices. The algorithm features a lightweight network architecture that follows the YOMO inference framework, combining the advantages of YOLO and MobileNetV3 in feature extraction and operational speed. Firstly, a lightweight attention module is introduced into MnasNet, and the improved MobileNetV3 is employed as the backbone network to enhance the feature extraction efficiency. Then, PANet and SPPnet are incorporated to aggregate the features from multiple effective feature layers. Subsequently, to reduce latency and improve efficiency, YOMO-Runwaynet generates a single optimal prediction for each object, eliminating the need for non-maximum suppression (NMS). Finally, experimental results on embedded devices demonstrate that YOMO-Runwaynet achieves a detection accuracy of over 89.5% on the ATD (Aerovista Runway Dataset), with a pixel error rate of less than 0.003 for runway keypoint detection, and an inference speed exceeding 90.9 FPS. These results indicate that the YOMO-Runwaynet algorithm offers high accuracy and real-time performance, providing effective support for the visual navigation of fixed-wing aircraft.","PeriodicalId":507567,"journal":{"name":"Drones","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An All-Time Detection Algorithm for UAV Images in Urban Low Altitude 城市低空无人机图像的全时检测算法

Drones Pub Date : 2024-07-18 DOI: 10.3390/drones8070332

Yuzhuo Huang, Jingyi Qu, Haoyu Wang, Jun Yang

{"title":"An All-Time Detection Algorithm for UAV Images in Urban Low Altitude","authors":"Yuzhuo Huang, Jingyi Qu, Haoyu Wang, Jun Yang","doi":"10.3390/drones8070332","DOIUrl":"https://doi.org/10.3390/drones8070332","url":null,"abstract":"With the rapid development of urban air traffic, Unmanned Aerial Vehicles (UAVs) are gradually being widely used in cities. Since UAVs are prohibited over important places in Urban Air Mobility (UAM), such as government and airports, it is important to develop air–ground non-cooperative UAV surveillance for air security all day and night. In the paper, an all-time UAV detection algorithm based on visible images during the day and infrared images at night is proposed by our team. We construct a UAV dataset used in urban visible backgrounds (UAV–visible) and a UAV dataset used in urban infrared backgrounds (UAV–infrared). In the daytime, the visible images are less accurate for UAV detection in foggy environments; therefore, we incorporate a defogging algorithm with the detection network that can ensure the undistorted output of images for UAV detection based on the realization of defogging. At night, infrared images have the characteristics of a low-resolution, unclear object contour, and complex image background. We integrate the attention and the transformation of space feature maps into depth feature maps to detect small UAVs in images. The all-time detection algorithm is trained separately on these two datasets, which can achieve 96.3% and 94.7% mAP50 on the UAV–visible and UAV–infrared datasets and perform real-time object detection with an inference speed of 40.16 FPS and 28.57 FPS, respectively.","PeriodicalId":507567,"journal":{"name":"Drones","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Control and Application of Tree Obstacle-Clearing Coaxial Octocopter with Flexible Suspension Saw 带柔性悬挂锯的树木障碍物清除同轴八旋翼飞行器的控制与应用

Drones Pub Date : 2024-07-17 DOI: 10.3390/drones8070328

Luwei Liao, Zhong Yang, Haoze Zhuo, Nuo Xu, Wei Wang, Kun Tao, Jiabing Liang, Qiuyan Zhang

引用次数: 0

Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter Spectrometer 使用声光可调谐滤波光谱仪实时注册无人机高光谱遥感图像

Drones Pub Date : 2024-07-17 DOI: 10.3390/drones8070329

Hong Liu, Bingliang Hu, Xingsong Hou, Tao Yu, Zhoufeng Zhang, Xiao Liu, Jiacheng Liu, Xueji Wang

{"title":"Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter Spectrometer","authors":"Hong Liu, Bingliang Hu, Xingsong Hou, Tao Yu, Zhoufeng Zhang, Xiao Liu, Jiacheng Liu, Xueji Wang","doi":"10.3390/drones8070329","DOIUrl":"https://doi.org/10.3390/drones8070329","url":null,"abstract":"Differences in field of view may occur during unmanned aerial remote sensing imaging applications with acousto-optic tunable filter (AOTF) spectral imagers using zoom lenses. These differences may stem from image size deformation caused by the zoom lens, image drift caused by AOTF wavelength switching, and drone platform jitter. However, they can be addressed using hyperspectral image registration. This article proposes a new coarse-to-fine remote sensing image registration framework based on feature and optical flow theory, comparing its performance with that of existing registration algorithms using the same dataset. The proposed method increases the structure similarity index by 5.2 times, reduces the root mean square error by 3.1 times, and increases the mutual information by 1.9 times. To meet the real-time processing requirements of the AOTF spectrometer in remote sensing, a development environment using VS2023+CUDA+OPENCV was established to improve the demons registration algorithm. The registration algorithm for the central processing unit+graphics processing unit (CPU+GPU) achieved an acceleration ratio of ~30 times compared to that of a CPU alone. Finally, the real-time registration effect of spectral data during flight was verified. The proposed method demonstrates that AOTF hyperspectral imagers can be used in real-time remote sensing applications on unmanned aerial vehicles.","PeriodicalId":507567,"journal":{"name":"Drones","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Control of Helicopter Using Virtual Swashplate 利用虚拟斜盘控制直升机

Drones Pub Date : 2024-07-16 DOI: 10.3390/drones8070327

J. Flores, Sergio Salazar, I. González-Hernández, Yukio Rosales, Rogelio Lozano, Eduardo Salazar, Benjamin Nicolas

引用次数: 0

Multi-UAVs Tracking Non-Cooperative Target Using Constrained Iterative Linear Quadratic Gaussian 使用受限迭代线性二次高斯跟踪非合作目标的多无人机

Drones Pub Date : 2024-07-15 DOI: 10.3390/drones8070326

Can Zhang, Yidi Wang, Wei Zheng

{"title":"Multi-UAVs Tracking Non-Cooperative Target Using Constrained Iterative Linear Quadratic Gaussian","authors":"Can Zhang, Yidi Wang, Wei Zheng","doi":"10.3390/drones8070326","DOIUrl":"https://doi.org/10.3390/drones8070326","url":null,"abstract":"This study considers the problem of controlling multi-unmanned aerial vehicles (UAVs) to consistently track a non-cooperative ground target with uncertain motion in a hostile environment with obstacles. An active information acquisition (AIA) problem is formulated to minimize the uncertainty of the target tracking task. The uncertain motion of the target is represented as a Wiener process. First, we optimize the configuration of the UAV swarm considering the collision avoidance, horizontal field of view (HFOV), and communication radius to calculate the reference trajectories of the UAVs. Next, a novel algorithm called Constrained Iterative Linear Quadratic Gaussian (CILQG) is introduced to track the reference trajectory. The target’s state with uncertainty and the UAV state are described as beliefs. The CILQG algorithm utilizes the Unscented Transform to propagate the belief regarding the UAVs’ motions, while also accounting for the impact of navigation errors on the target tracking process. The estimation error of the target position of the proposed method is under 4 m, and the error of tracking the reference trajectories is under 3 m. The estimation error remains unchanged even in the presence of obstacles. Therefore, this approach effectively deals with the uncertainties involved and ensures accurate tracking of the target.","PeriodicalId":507567,"journal":{"name":"Drones","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems 基于无人机的无上行链路赠送 NOMA 系统的消息传递检测器

Drones Pub Date : 2024-07-14 DOI: 10.3390/drones8070325

Yi Song, Yiwen Zhu, Kun Chen-Hu, Xinhua Lu, Peng Sun, Zhongyong Wang

{"title":"Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems","authors":"Yi Song, Yiwen Zhu, Kun Chen-Hu, Xinhua Lu, Peng Sun, Zhongyong Wang","doi":"10.3390/drones8070325","DOIUrl":"https://doi.org/10.3390/drones8070325","url":null,"abstract":"Utilizing unmanned aerial vehicles (UAVs) as mobile access points or base stations has emerged as a promising solution to address the excessive traffic demands in wireless networks. This paper investigates improving the detector performance at the unmanned aerial vehicle base stations (UAV-BSs) in an uplink grant-free non-orthogonal multiple access (GF-NOMA) system by considering the activity state (AS) temporal correlation of the different user equipments (UEs) in the time domain. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared to existing methods while keeping the same order complexity.","PeriodicalId":507567,"journal":{"name":"Drones","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event-Triggered Collaborative Fault Diagnosis for UAV–UGV Systems 无人机-无人潜航器系统的事件触发协同故障诊断

Drones Pub Date : 2024-07-13 DOI: 10.3390/drones8070324

Runze Li, Bin Jiang, Yan Zong, N. Lu, Li Guo

{"title":"Event-Triggered Collaborative Fault Diagnosis for UAV–UGV Systems","authors":"Runze Li, Bin Jiang, Yan Zong, N. Lu, Li Guo","doi":"10.3390/drones8070324","DOIUrl":"https://doi.org/10.3390/drones8070324","url":null,"abstract":"The heterogeneous unmanned system, which is composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV), has been broadly applied in many domains. Collaborative fault diagnosis (CFD) among UAVs and UGVs has become a key technology in these unmanned systems. However, collaborative fault diagnosis in unmanned systems faces the challenges of the dynamic environment and limited communication bandwidth. This paper proposes an event-triggered collaborative fault diagnosis framework for the UAV–UGV system. The framework aims to achieve autonomous fault monitoring and cooperative diagnosis among unmanned systems, thus enhancing system security and reliability. Firstly, we propose a fault trigger mechanism based on broad learning systems (BLS), which utilizes sensor data to accurately detect and identify faults. Then, under the dynamic event triggering mechanism, the network communication topology between the UAV–UGV system and BLS is used to achieve cooperative fault diagnosis. To validate the effectiveness of our proposed scheme, we conduct experiments on a software-in-the-loop (SIL) simulation platform. The experimental results demonstrate that our method achieves high diagnosis accuracy for the UAV–UGV system.","PeriodicalId":507567,"journal":{"name":"Drones","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141651950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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