2022年4月22日 · In this study, we create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) and labeling the bounding box of each plane on the images. HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry ...

Through transfer learning and the airplane samples we collected from Google Earth, we have implemented an end-to-end trainable airplane detection framework. We add a rotation operation to increase the diversity of training samples during data augmentation.

This repo contains the details of the HRPlanesv2, a high-resolution satellite imagery dataset for aircraft detection, as well as the benchmark results of experiments using YOLOv4, YOLOv5, and YOLOv...

2022年10月1日 · In this paper, we provide an in-depth review of deep learning methods for object detection applied to aircraft detection including both conventional and state-of-the-art techniques.

2024年12月17日 · This study presents a comprehensive evaluation of aircraft detection algorithms in satellite imagery, namely YOLO (v5 and v8), Faster RCNN, CenterNet, RetinaNet, RTMDet and DETR, specifically focusing on the HRPlanesV2 dataset and extending the assessment to subsets of the GDIT dataset: train, test, and validation.

2024年4月3日 · This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery.

In this paper, we propose an airplane detection method using convolutional neural networks (CNNs) in a coarse-to-fine manner which simulates the detection manner of image analysts. Our method proposes coarse candidate regions containing multiple airplanes first, then finely detects each airplane in these candidate regions.

2020年1月1日 · In this paper, an effective method for aircraft detection in high-resolution remote sensing image is proposed based on corner clustering and convolutional neural network (CNN). Firstly, image binary segmentation is used to minimize background interference.

Aircraft detection in remote sensing images is always the research hotspot but a challenging task for the variations of aircraft type, pose, size and complex background. The paper proposes a region-based convolutional neural network to detect aircrafts.

2023年5月9日 · We propose a multi-task joint training method for remote sensing aircraft detection, within which landmark detection provides stronger semantic structural features for bounding box localization in dense areas, which helps to improve the accuracy of aircraft detection and recognition;