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.
翻译:五百公尺外观射频谱望远镜(FAST)是世界上最大的单片射电望远镜,其面积庞大的反射表面具有前所未有的敏感性,但很容易损坏,例如自然引发的坠落物体造成的凹痕和洞洞。因此,及时和准确地探测地表缺陷对于FAST的稳定运作至关重要。常规手工检查涉及人体检查员攀升和检查大型表面,这是一个耗时且可能不可靠的过程。为了加快检查进程并提高其准确性,这项工作是使FAST检查自动化的第一步,办法是将深层学习技术与无人机技术相结合。首先,无人驾驶飞机沿预定路线飞过地面。由于表面缺陷在规模上差异很大,并显示高等级的相似性,直接应用现有的深层探测器来探测无人机图像上的缺陷非常容易丢失和误认缺陷。作为一种补救措施,我们引入了交叉融合,专门用于深层探测器的插座操作,使多层次特征的适应性融合能够根据局部的选择性方式进行。因此,基于当地缺陷模式的精确度和精确性机能定位的精确性机级定位,因此,我们各种机级的准确性机级测试和精确性机级机级的机级的精确性机级机级机级的精确性机级的精确性定位是。