Abstract
Automatic water gauge reading is very important for cargo weighting in ocean transportation. In this process, accurate waterline extraction is an important yet challenging step. Waterline extraction is subjected to many environmental interference factors, e.g., bad illumination, bad weather conditions, ambiguous contours of water stain, etc. In this paper, we propose a joint multitask based deep model for accurate waterline extraction. The proposed model consists of two main branches. One branch is used to extract high-level contextual information. The other branch rooted in shallow layers is used to extract low-level detail features. The two branches are later coupled with each other to co-supervise the estimation of waterline. Our model works well on various conditions, such as uneven light, serious reflections, etc. We also introduce a new benchmark dataset for waterline extraction. This dataset consists of 360 pictures extracted from 69 videos collected in several actual ports. Furthermore, sufficient experiments show that our model is effective on the introduced dataset and outperforms the state-of-the-art methods.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61976208) and Coal Science and Technology Research Institute Co., Ltd Technological Innovation Project(2019CX-I-03).
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Chen, Y., Ni, B., Meng, G., Sha, B. (2022). Joint Semantic Segmentation and Edge Detection for Waterline Extraction. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_29
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