Ship detection in SAR images based on an improved faster R-CNN
Naval Aeronautical and Astronautical University
Abstract
Deep learning has led to impressive performance on a variety of object detection tasks recently. But it is rarely applied in ship detection of SAR images. The paper aims to introduce the detector based on deep learning into this field. We analyze the advantages of the state-of-the-art Faster R-CNN detector in computer vision and limitations in our specific domain. Given this analysis, we proposed a new dataset and four strategies to improve the standard Faster R-CNN algorithm. The dataset contains ships in various environments, such as image resolution, ship size, sea condition, and sensor type, it can be a benchmark for researchers to evaluate their algorithms. The strategies include feature fusion, transfer…
Citation impact
- FWCI
- 6.93
- Percentile
- 100%
- References
- 21
Authors
3Topics & keywords
- Computer science
- Benchmark (surveying)
- Object detection
- Deep learning
- Artificial intelligence
- Focus (optics)
- Transfer of learning
- Detector
- Life below water