Multi-scale Fast Detection of Objects in High Resolution Remote Sensing Images
| Autor: | Wan-Dong Jiang, Ling Han, Ming Cong, Yun Yang, Jiangbo Xi, Long-Wei Li |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Deep learning 0211 other engineering and technologies High resolution 02 engineering and technology Automatic learning Rapid detection Object detection Feature design Robustness (computer science) Object type Artificial intelligence business 021101 geological & geomatics engineering Remote sensing |
| Zdroj: | 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC). |
| DOI: | 10.1109/icivc50857.2020.9177484 |
| Popis: | Objects detection in high resolution (HR) remote sensing images plays an important role in modern military, national defense, and commercial field. Because of a variety of object types and different sizes, it is difficulty to realize the rapid detection of multi-scale high resolution remote sensing objects, and provides support for succeeding decision making responses. This paper proposes a multi-scale fast detection method of remote sensing image objects with deep learning model, named YOLOv3. The COCO data model is used to establish the high resolution remote sensing image set based on the NWPU data. The proposed model can realize automatic learning of object features, which has good properties on generalization and robustness. It can also overcome the deficiency of traditional object detection method needing manual feature design for different objects. The experimental results show that the average detection accuracy of objects with different sizes in high resolution remote sensing images can reach 93.50%, which demonstrates that the proposed method can achieve rapid detection of different types of multi-scale objects. |
| Databáze: | OpenAIRE |
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