Fender segmentation in unmanned aerial vehicle images based on densely connected receptive field block
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초록
Periodic evaluation of port structures including outer walls and berthing structures with fenders is paramount to ensure their safety. However, most fenders are inaccessible on land and inspectors utilize floating boats for inspections. Therefore, this study aimed to develop a fender segmentation system incorporating a vision sensor with deep learning segmentation approach. The semantic segmentation model in an encoderedecoder framework was densely connected through a receptive field block convolution module each with two different dilation rates and classified various types and sizes of fenders efficiently. The images of various types of fenders were assembled and augmented to train the proposed network, and its performance was compared to conventional segmentation models. The trained network was finally applied to unmanned aerial vehicle images and the results showed that fenders were successfully segmented even in the case of the images that include changes in shapes or colors.
Abstract
Periodic evaluation of port structures including outer walls and berthing structures with fenders is paramount to ensure their safety. However, most fenders are inaccessible on land and inspectors utilize floating boats for inspections. Therefore, this study aimed to develop a fender segmentation system incorporating a vision sensor with deep learning segmentation approach. The semantic segmentation model in an encoderedecoder framework was densely connected through a receptive field block convolution module each with two different dilation rates and classified various types and sizes of fenders efficiently. The images of various types of fenders were assembled and augmented to train the proposed network, and its performance was compared to conventional segmentation models. The trained network was finally applied to unmanned aerial vehicle images and the results showed that fenders were successfully segmented even in the case of the images that include changes in shapes or colors.
참고문헌 (24)
- Boonpook, W., "Deep learning-based multi-feature semantic segmentation in building extraction from images of UAV photogrammetry", Int. J. Rem. Sens, 42(1), pp.1~19 (2021)
- Chen, L. C., "Rethinking Atrous Convolution for Semantic Image Segmentation", ~ (2017)
- Chen, L. C., "Searching for efficient multi-scale architectures for dense image prediction", Adv. Neural Inf. Process. Syst, 31 (2018)
- Girshick, R., "Fast R-CNN", Proceedings of IEEE International Conference on Computer Vision, pp.1440~1448 (2015)
- Girshick, R., "Rich feature hierarchies for accurate object detection and semantic segmentation", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.580~587 (2014)
- He, K., "Mask R-CNN", Proceedings of IEEE international conference on computer vision, pp.2961~2969 (2017)
- Kerkech, M., "Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach", Comput. Electron. Agric, 174, pp.105446~ (2020)
- Liu, W., "SSD: Single Shot Multibox Detector", European conference on computer vision, pp.21~37 (2016)
- Liu, S., "Receptive Field Block Net for Accurate and Fast Object Detection", Proceedings of European conference on computer vision, pp.385~400 (2018)
- Long, J., "Fully convolutional networks for semantic segmentation", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.3431~3440 (2015)
- Ministry of Land, Infrastructure and Transport, "Detailed Guidelines for Facility Safety and Maintenance (Performance Evaluation)", ~ (2018)
- Ministry of Oceans and Fisheries, "Detailed Guidelines for Safety Inspection of Port Facilities", ~ (2015)
- Ministry of Oceans and Fisheries, "Port and Fishing Port Fender Maintenance Manual", ~ (2018)
- Ministry of Oceans and Fisheries, "Port Facility Maintenance and Mid- to Long-Term Roadmap Establishment Service", ~ (2020)
- Ministry of Oceans and Fisheries, "Research and Service Report on Maintenance and System Improvement of Fender Systems (2021-2023)", ~ (2022)
- Redmon, J., "You only look once: unified, real-time object detection", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.779~788 (2016)
- Ren, S., "Faster R-CNN: towards real-time object detection with region proposal networks", Adv. Neural Inf. Process. Syst, pp.91~99 (2015)
- Ronneberger, O., "U-Net: convolutional networks for biomedical image segmentation", Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.234~241 (2015)
- Sakakibara, S., "Ship berthing and mooring monitoring system by pneumatic-type fenders", Ocean Eng, 34(8-9), pp.1174~1181 (2007)
- Simonyan, K., "Very deep convolutional networks for large-scale image recognition. abs", 1409.1556v6, 2015, ~
- Takahashi, N., "Densely connected multi-dilated convolutional networks for dense prediction tasks", Proceedings of the IEEE, 2021, 993~1002
- Wang, H., "Deformable non-local network for video super-resolution", IEEE Access, 7, pp.177734~177744 (2019)
- Yamada, S., "Final acceptance test on advanced ship maneuvering and mooring support system at ship-to-ship transfer operations by fender monitoring system of pneumatic fenders", Proceedings of 22nd International Offshore and Polar Engineering Conference, pp.901~908 (2012)
- Yang, M. D., "Semantic segmentation using deep learning with vegetation indices for rice lodging identification in multidate UAV visible images", Rem. Sens, 12(4), pp.633~ (2020)