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Journals / International Journal of Engineering and Geosciences / 2022 / Cilt: 7 - Sayı: 2

Divide and conquer object detection (DACOD) method for runway detection in remote sensing images

Atakan KÖREZ
Pages
154–160
Publication Date
2022-10-19
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The experimental results demonstrate that the proposed model yields good results (%81.5 mAP).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In recent years, parallel to the developments in satellite technology, obtaining and processing remote sensing images has become quite common.
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Key Findings & Data: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.
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Conclusion & Impact:The experimental results demonstrate that the proposed model yields good results (%81.5 mAP).
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Remote Sensing · Runway Detection · Convolutional Neural Network

Abstract

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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Cite This Article

KÖREZ, A. (2022). Divide and conquer object detection (DACOD) method for runway detection in remote sensing images. International Journal of Engineering and Geosciences, Cilt: 7 - Sayı: 2, 154–160. https://ajindex.com/articles/540671

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References (10)

  1. Akbar J, Shahzad M, Malik MI, Ul-Hasan A & Shafai F (2019). Runway Detection and Localization in Aerial Images using Deep Learning. Digital Image Computing: Techniques and Applications (DICTA), 1 -8, Perth,Australia. Aytekin Ö, Zöngür U & Halici U (2013). Texture-Based Airport Runway Detection. IEEE Geoscience and Remote Sensing Letters, 10-(3), 471-475.
  2. Bengio Y, Goodfellow I & Courville A (2016). Deep Learning. MIT Press. ISBN: 978-0262035613. Cheng G & Han J (2016). A Survey on Object Detection in Optical Remote Sensing Images. ISPRS Journal of Photogrammetry and Remote Sensing,117, 11-28.
  3. Ding J, Xue N, Long Y, Xia G & Lu Q (2019). Learning RoI Transformer for Oriented Object Detection in Aerial Images. IEEE Conference Computer Vision and Pattern Recognition (CVPR), 2844 -2853, Seul, Korea. He K, Zhang X, Ren S & Sun J (2016). Deep Residual Learning for Image Recognition. IEEE Conference Computer Vision and Pattern Recognition (CVPR), 770 -778, Nevada, USA. He K, Gkioxari G, Dollar P & Girshick G (2017). Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), 324-333,
  4. Venice, Italy. Ju M, Luo J, Zhang P, He M & Luo H (2019). A Simple and Efficient Network for Small Target Detection. IEEE Access, 7, 85771-85781.
  5. Li Z, Liu Z & Shi W (2014). Semiautomatic Airport Runway Extraction Using a Line-Finder-Aided Level Set Evolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12), 4738-4749.
  6. Lin T Y, Goyal P, Girshick R, He K & Dollar P (2017). Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 99, 2999-3007.
  7. Liu Z, Yuan L, Weng L & Yang Y A (2017). High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines. 6th International Conference on Pattern Recognition Applications and Methods, 324 – 333, Porto, Portugal. Lv W, Dai K, Wu L, Yang X & Xu W (2018). Runway Detection in SAR Images Based on Fusion Sparse Representation and Semantic Spatial Matching. IEEE Access, 6, 27984-27992.
  8. Krizhevsky A, Sutskever I & Hinton G E (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
  9. Ren S, He K, Girshick R & Sun J (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39-(6), 91–99.
  10. Song Q, Yang F, Yang L, Liu C, Hu M & Xia L (2021). Learning Point-Guided Loc