Abstract
Semantic segmentation in the water scene is significant for water environment monitoring. Recent water scene segmentation methods usually regard all floating objects as only one foreground category that limits the understanding of the water scene. Considering various floating objects, we propose a Detail Perception Network (DPNet) to address two challenges for water scene segmentation with multi-categories floating objects. One is the sample imbalance among objects of different scales, which leads to low accuracy on small objects covering a few pixel samples. Another is the weak discriminability of features among the categories that are close to the blurred edge, which leads to the miss-segmentation in the blurred edge region. For sample imbalance, we design Distance Field Loss (DF Loss) to strengthen the learning of small objects by a pixel-wise weight calculated from a distance field during training. To address the weak discriminability of features among categories that are close to the blurred edge, we propose a Category Edge Perception Pyramid (CEPP) module that learns the edge feature of each category as prior knowledge to enhance edge features. For training and evaluating relative models, we also establish a dataset named ColorWater, which contains 1279 images with 9 semantic labels over various water scenes. Extensive experiments demonstrate that our model performs favorably against the state-of-the-art models on our ColorWater dataset and the public Aeroscapes dataset.
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Liang, C., Cai, W., Peng, S., Liu, Q. (2022). Detail Perception Network for Semantic Segmentation in Water Scenes. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13282. Springer, Cham. https://doi.org/10.1007/978-3-031-05981-0_15
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