Survey and Evaluation of Sensors for Overhead Cable Detection using UAVs

Malle, Nicolaj Haarhøj; Nyboe, Frederik Falk; Ebeid, Emad

doi:10.1109/icuas51884.2021.9476724

Cited by 10 publications

(7 citation statements)

References 16 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Choosing the right sensors for the task is crucial to ensure that the drone remains lightweight. Therefore, we have compared suitable sensors to determine their relative performances and impacts several parameters [33]. Based on this study, a mmWave radar sensor is chosen as the primary sensor to perform overhead cable detection.…”

Section: Sensors For Cable Detectionmentioning

confidence: 99%

Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure

et al. 2023

Self Cite

View full text Add to dashboard Cite

Inspection of critical infrastructure with drones is experiencing an increasing uptake in the industry driven by a demand for reduced cost, time, and risk for inspectors. Early deployments of drone inspection services involve manual drone operations with a pilot and do not obtain the technological benefits concerning autonomy, coordination, and cooperation. In this paper, we study the design needed to handle the complexity of an Unmanned Aerial System (UAS) to support autonomous inspection of safety-critical infrastructure. We apply a constructive research approach to link innovation needs with concepts, designs, and validations that include simulation and demonstration of key design parts. Our design approach addresses the complexity of the UAS and provides a selection of technology components for drone and ground control hardware and software including algorithms for autonomous operation and interaction with cloud services. The paper presents a drone perception system with accelerated onboard computing, communication technologies of the UAS, as well as algorithms for swarm membership, formation flying, object detection, and fault detection with artificial intelligence. We find that the design of a cooperative drone swarm and its integration into a custom-built UAS for infrastructure inspection is highly feasible given the current state of the art in electronic components, software, and communication technology.

show abstract

Section: Sensors For Cable Detectionmentioning

confidence: 99%

Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…These use some combination of monocular and stereo cameras to detect the pipe-like structures. However, as shown in [16], the performance of stereo depth perception of thin powerline cables deteriorates heavily within a few meters. Additionally, for a purely monocular approach, the diameters of each powerline cable must be known in order to obtain a depth estimate for the powerlines.…”

Section: Related Workmentioning

confidence: 99%

“…Compared to sensors of similar price, weight, and size, our previous work [16] shows that a mmWave radar device outperforms a range of other sensors when detecting multiple powerlines from onboard a UAV. Particularly the signal-tonoise ratio of the produced data and the detection range stand out.…”

Section: Related Workmentioning

confidence: 99%

“…The depth accuracy of the perception system is mostly dependent on the accuracy of the incoming mmWave radar data. We have compared the accuracy of mmWave radar data with other sensors such as LiDAR and stereo cameras in our previous work on sensors for powerline detection [16]. A simple indoor ground truth measurement was conducted by placing the perception system equipped drone beneath a section of suspended powerline cable.…”

Section: A Perception Systemmentioning

confidence: 99%

See 1 more Smart Citation

Onboard Powerline Perception System for UAVs Using mmWave Radar and FPGA-Accelerated Vision

2022

Self Cite

View full text Add to dashboard Cite

Autonomous Unmanned Aerial Vehicle (UAV) interactions with powerlines, such as close-up inspections for fault detection or grasping and landing for recharging, require advanced onboard perception capabilities. To solve such tasks, the UAV must be equipped with perception abilities that allow it to navigate between powerlines and safely approach specific cables of interest. A perception system with such capabilities requires state-of-the-art sensor technologies and data processing while still being subject to the limited hardware and energy resources of the UAV. In this paper, we present an advanced embedded system based on the cutting-edge Multiprocessing System-on-Chip (MPSoC) for onboard UAV powerline perception. Our platform consists of a mmWave radar and an RGB camera with data processing carried out on the MPSoC, covering both CPU and Field-Programmable Gate Array (FPGA) computations. Following hardware-software co-design methodology, the heavy image processing tasks are accelerated in the FPGA and fused with computationally light mmWave data on the CPU, facilitating pose-estimation of the power lines. Utilizing the open-source autonomy frameworks PX4 and ROS2, we demonstrate integration of the system with onboard path planning based on the estimated cable positions. The robustness of the detection and pose-estimation methods have been demonstrated in several tests performed both in simulated and real-world powerline environments. The results show that our proposed perception system allows the UAV to safely navigate in close proximity to powerlines, by perceiving more individual cables at longer distances compared to previous work, while remaining lightweight, power-efficient, and low-cost.

show abstract

“…Radar and LiDAR are common ranging sensors, but their size, weight, cost, and power consumption make them more suitable for large-scale UAVs. 11 The monocular camera could be the cheapest among these sensors, but the limitation is that the ranging information is not directly measured. In this study, a sophisticated vision-based localization framework is explored using a monocular camera.…”

Section: Introductionmentioning

confidence: 99%

Visual localization with a monocular camera for unmanned aerial vehicle based on landmark detection and tracking using YOLOv5 and DeepSORT

Dong²,

Zhao

et al. 2023

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Absolute visual localization is of significant importance for unmanned aerial vehicles when the satellite-based localization system is not available. With the rapid evolution in the field of deep learning, the real-time visual detection and tracking of landmarks by an unmanned aerial vehicle could be implemented onboard. This study demonstrates a landmark-based visual localization framework for unmanned aerial vehicles flying at low altitudes. YOLOv5 and DeepSORT are used for multi-object detection and tracking, respectively. The unmanned aerial vehicle localization is achieved according to the geometric similarity between the geotagged transmission towers and the annotated images captured by a monocular camera. The validation is accomplished both in the Rflysim-based simulation and the quadrotor-based real flight. The localization precision is about 10 m, and the location update frequency reaches 5 Hz with a commercially available entry-level edge artificial intelligence platform. The proposed visual localization strategy needs no satellite image as a reference map, which saves a significant amount of the GPU memory and makes possible the end-to-end implementation on small unmanned aerial vehicles.

show abstract

Survey and Evaluation of Sensors for Overhead Cable Detection using UAVs

Cited by 10 publications

References 16 publications

Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure

Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure

Onboard Powerline Perception System for UAVs Using mmWave Radar and FPGA-Accelerated Vision

Visual localization with a monocular camera for unmanned aerial vehicle based on landmark detection and tracking using YOLOv5 and DeepSORT

Contact Info

Product

Resources

About