The purpose of this research is development of vision-based object detection algorithm that recognizes a marine object, localizes the object on captured frames, and estimates the distance to the object. Faster R-CNN and stereo vision based depth estimation are combined for real-time marine object detection. The performance of this algorithm is verified by model ship detection test in towing tank. The test results showed that this algorithm is potentially applicable to real USV.
|Number of pages||12|
|Publication status||Published - 4 Dec 2018|
|Event||World Maritime Technology Conference 2018 - Renaissance Shanghai Zhongshan, Part Hotel Shanghai, Shanghai, China|
Duration: 4 Dec 2018 → 7 Dec 2018
Conference number: 6
|Conference||World Maritime Technology Conference 2018|
|Period||4/12/18 → 7/12/18|
- unmanned surface vehicle
- vision-based object detection
- aster region with convolutional neural network
- depth estimation
Kim, H., Boulougouris, E., & Kim, S-H. (2018). Object detection algorithm for unmanned surface vehicle using faster R-CNN. Paper presented at World Maritime Technology Conference 2018, Shanghai, China.