Object detection in remote sensing images has been frequently used in a wide range of areas such as land planning, city monitoring, traffic monitoring, and agricultural applications. It is essential in the field of aerial and satellite image analysis but it is also a challenge. To overcome this challenging problem, there are many object detection models using convolutional neural networks (CNN). The deformable convolutional structure has been introduced to eliminate the disadvantage of the fixed grid structure of the convolutional neural networks. In this study, a multi-scale Faster R-CNN method based on deformable convolution is proposed for single/low graphics processing unit (GPU) systems. Weight standardization (WS) is used instead of batch normalization (BN) to make the proposed model more efficient for a small batch size (1 img/per GPU) on single GPU systems. Experiments were conducted on the publicly available 10-class geospatial object detection (NWPU-VHR 10) dataset to evaluate the object detection performance of the proposed model. Experiment results show that our model achieved a 92.3 mAP. This is a 1.7% mAP increase when compared to the best results in the models using the same dataset.
The detection of objects in very high-resolution (VHR) remote sensing images has become increasingly popular with the enhancement of remote sensing technologies. High-resolution images from aircrafts or satellites contain highly detailed and mixed backgrounds that decrease the success of object detection in remote sensing images. In this study, a model that performs weighted ensemble object detection using optimized coefficients is proposed. This model uses the outputs of three different object detection models trained on the same dataset. The model’s structure takes two or more object detection methods as its input and provides an output with an optimized coefficient-weighted ensemble. The Northwestern Polytechnical University Very High Resolution 10 (NWPU-VHR10) and Remote Sensing Object Detection (RSOD) datasets were used to measure the object detection success of the proposed model. Our experiments reveal that the proposed model improved the Mean Average Precision (mAP) performance by 0.78%–16.5% compared to stand-alone models and presents better mean average precision than other state-of-the-art methods (3.55% higher on the NWPU-VHR-10 dataset and 1.49% higher when using the RSOD dataset).
In recent years, parallel to the developments in satellite technology, obtaining and processing remote sensing images has become quite common. While airports are the first points to be targeted by enemy forces in times of war, they are very critical points in times of peace due to their significance for transportation, trade, and economy networks. The runways are the most distinctive feature of airports. There are many studies on detecting the runways in remote sensing images (RSIs). However, existing methods for detecting the runway objects that have an excessive width in high-resolution (4137 x 4552 pixels and above) RSIs may be insufficient. In this study, a Divide and Conquer Object Detection (DACOD) method is proposed for the runway objects that have an excessive width in high-resolution RSIs. In the proposed method, images are divided into images of 1024 x 1024 pixels, and the runway objects in these images are detected as oriented. Then, the detection results are merged by using the angles and the final runway detection results are obtained. The experimental results demonstrate that the proposed model yields good results (%81.5 mAP). This is an 11% mAP increase when compared to the best results in The State of The Art (SOTA) object detection models using the same dataset.
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