Abstract
An essential component for the autonomous flight or air-to-ground surveillance of a UAV is an object detection device. It must possess a high detection accuracy and requires real-time data processing to be employed for various tasks such as search and rescue, object tracking and disaster analysis. With the recent advancements in multimodal data-based object detection architectures, autonomous driving technology has significantly improved, and the latest algorithm has achieved an average precision of up to 96%. However, these remarkable advances may be unsuitable for the image processing of UAV aerial data directly onboard for object detection because of the following major problems: (1) Objects in aerial views generally have a smaller size than in an image and they are uneven and sparsely distributed throughout an image; (2) Objects are exposed to various environmental changes, such as occlusion and background interference; and (3) The payload weight of a UAV is limited. Thus, we propose employing a new real-time onboard object detection architecture, an RGB aerial image and a point cloud data (PCD) depth map image network (RGDiNet). A faster region-based convolutional neural network was used as the baseline detection network and an RGD, an integration of the RGB aerial image and the depth map reconstructed by the light detection and ranging PCD, was utilized as an input for computational efficiency. Performance tests and evaluation of the proposed RGDiNet were conducted under various operating conditions using hand-labeled aerial datasets. Consequently, it was shown that the proposed method has a superior performance for the detection of vehicles and pedestrians than conventional vision-based methods.
Highlights
Our approach can significantly reduce the computational and improve the detection efficiency; (2) Using the histogram network (HN), the actual distance between a costs and improve the detection efficiency; (2) Using the HN, the actual distance between predicted object and a Unmanned aerial vehicles (UAVs) can be provided; and (3) The 3D point cloud data (PCD) can be used with sima predicted object and a UAV can be provided; and (3) The 3D PCD can be used with ultaneous localization and mapping (SLAM) [40] for object positioning along with the simultaneous localization and mapping (SLAM) [40] for object positioning along with surrounding environment, if necessary, and it has robustness against changes in the the surrounding environment, if necessary, and it has robustness against changes in the imaging conditions and various environments
B channels, the less affected channel is substituted by a depth map created by the PCD to reconstruct the input for the faster R-convolutional neural networks (CNNs)
Expected to be practical by expanding the proposed object detection scheme to various channels, the less affected channel is substituted by a depth map created by the PCD to fields, such as autonomous flying drones and ground surveillance from an air-to-ground reconstruct the input for the faster region-based CNN (R-CNN)
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. One of the most important components for the autonomous flights or air-to-ground surveillance of UAVs is object detection, which requires real-time data processing with high accuracy. It is directly utilized in diverse tasks such as situation analysis, traffic monitoring, disaster analysis and object tracking. To resolve the ambiguities of viewpoints and visual distances, [34] proposed a scale adaptive proposal network and [35] suggested utilizing reinforcement learning to sequentially select the areas to be detected on a high-resolution scale These methods aim to efficiently detect vehicles or pedestrians on roads and accurately count the number of objects, and can be employed for the detection of objects in aerial images.
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