Automated optical inspection of FAST’s reflector surface using drones and computer vision  

作　　者：Jianan Li Shenwang Jiang Liqiang Song Peiran Peng Feng Mu Hui Li Peng Jiang Tingfa Xu 

机构地区：[1]Beijing Institute of Technology,Beijing 100081,China [2]National Astronomical Observatories of China,Beijing 100107,China [3]Beijing Institute of Technology Chongqing Innovation Center,Chongqing,401135,China

出　　处：《Light(Advanced Manufacturing)》2023年第1期1-11,共11页光（先进制造）（英文）

基　　金：financially supported by the National Natural Science Foundation of China(No.62101032);the Postdoctoral Science Foundation of China(Nos.2021M690015,2022T150050);the Beijing Institute of Technology Research Fund Program for Young Scholars(No.3040011182111).

摘　　要：The Five-hundred-meter Aperture Spherical radio Telescope(FAST)is the world’s largest single-dish radio telescope.Its large reflecting surface achieves unprecedented sensitivity but is prone to damage,such as dents and holes,caused by naturally-occurring falling objects.Hence,the timely and accurate detection of surface defects is crucial for FAST’s stable operation.Conventional manual inspection involves human inspectors climbing up and examining the large surface visually,a time-consuming and potentially unreliable process.To accelerate the inspection process and increase its accuracy,this work makes the first step towards automating the inspection of FAST by integrating deep-learning techniques with drone technology.First,a drone flies over the surface along a predetermined route.Since surface defects significantly vary in scale and show high inter-class similarity,directly applying existing deep detectors to detect defects on the drone imagery is highly prone to missing and misidentifying defects.As a remedy,we introduce cross-fusion,a dedicated plug-in operation for deep detectors that enables the adaptive fusion of multi-level features in a point-wise selective fashion,depending on local defect patterns.Consequently,strong semantics and fine-grained details are dynamically fused at different positions to support the accurate detection of defects of various scales and types.Our AI-powered drone-based automated inspection is time-efficient,reliable,and has good accessibility,which guarantees the long-term and stable operation of FAST.

关 键 词：FAST DRONE Deep learning Feature fusion 

分 类 号：O43[机械工程—光学工程]