Abstract
The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.
Highlights
Electrical and mechanical (E&M) devices are crucial to the efficient operation of the public transportation system
With the support of unmanned aerial vehicles (UAVs), aerial photography can be performed by the manual flight and viewing images of the target devices from the camera, the whole pipeline of maneuvering the UAV is still complicated and energy-consuming for the pilots. To address all these issues, in this work, we focus on the autonomous inspection quadrotor system without requiring prior knowledge of the target device and environment
The 2-D object detection model deployed on our quadrotor platform is trained by an open-source Darknet framework offline on an Intel Core i5-4690 CPU and two NVIDIA GeForce GRX TITAN Black graphic cards
Summary
Electrical and mechanical (E&M) devices are crucial to the efficient operation of the public transportation system. With the rapid advancement of computer vision technologies, unmanned aerial vehicles (UAVs), especially quadrotors, have been applied to many aspects recently, such as aerial photography [1], device inspection [2,3,4], search and rescue [5], and so on. Benefitting from their mobility, flexibility, and multi-functionality, UAVs have shown significant potential as a cost-efficient solution for routine visual inspection. Several traditional computer vision techniques were used to detect objects, and an Extended Kalman Filter (EKF) method was applied to estimate the position of objects. The “learning-based” refers to the object detection method embedded in the perception module, which is a supervised learning algorithm
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