Deep learning-based marker detection for autonomous drone landing is widely studied, due to its superior detection performance. However, no study was reported to address non-uniform motion-blurred input images, and most of the previous handcrafted and deep learning-based methods failed to operate with these challenging inputs. To solve this problem, we propose a deep learning-based marker detection method for autonomous drone landing, by (1) introducing a two-phase framework of deblurring and object detection, by adopting a slimmed version of deblur generative adversarial network (DeblurGAN) model and a You only look once version 2 (YOLOv2) detector, respectively, and (2) considering the balance between the processing time and accuracy of the system. To this end, we propose a channel-pruning framework for slimming the DeblurGAN model called SlimDeblurGAN, without significant accuracy degradation. The experimental results on the two datasets showed that our proposed method exhibited higher performance and greater robustness than the previous methods, in both deburring and marker detection.

中文翻译：

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基于SlimDeblurGAN的运动去模糊和标记检测，用于自主无人机着陆。

由于其卓越的检测性能，基于深度学习的用于自主无人机着陆的标记检测得到了广泛的研究。但是，尚无针对非均匀运动模糊输入图像的研究的报道，并且以前的大多数手工制作和基于深度学习的方法都无法在这些具有挑战性的输入中使用。为了解决这个问题，我们提出了一种基于深度学习的自主无人机着陆的标记检测方法，方法是（1）通过采用简化版本的去模糊生成对抗网络（DeblurGAN）模型引入去模糊和对象检测的两阶段框架和您仅分别看一次版本2（YOLOv2）检测器，以及（2）考虑到处理时间和系统精度之间的平衡。为此，我们提出了一种信道精简框架，用于精简称为SlimDeblurGAN的DeblurGAN模型，而不会显着降低精度。在两个数据集上的实验结果表明，我们提出的方法在去毛刺和标记检测方面都比以前的方法表现出更高的性能和更大的鲁棒性。