Publications by authors named "Noi Quang Truong"
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.
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- The study focuses on enhancing the detection and classification of various road markings, particularly arrows and bike markings, which are crucial for the safe operation of autonomous vehicles.
- A new method using a deep convolutional neural network called RetinaNet was proposed to tackle this challenge in different complex environments.
- Testing on three diverse datasets demonstrated that the RetinaNet approach provided superior accuracy and faster processing times compared to existing methods for recognizing road markings.
Autonomous landing of an unmanned aerial vehicle or a drone is a challenging problem for the robotics research community. Previous researchers have attempted to solve this problem by combining multiple sensors such as global positioning system (GPS) receivers, inertial measurement unit, and multiple camera systems. Although these approaches successfully estimate an unmanned aerial vehicle location during landing, many calibration processes are required to achieve good detection accuracy.
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