MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection - Jianhua Ye; Pan Li; Yunda Zhang; Ze Guo; Shoujin Zeng; Youji Zhan (Scientific Reports)
Abstract The capacity to detect shorelines is critical for the autonomous navigation of Unmanned Surface Vehicles (USVs). The majority of extant methods are unable to adapt to the discrimination of high similarity features between the shore and reflections in complex and diverse environments. Moreover, they are also incapable of accurately extracting fuzzy boundaries caused by different scenes and climatic conditions. To address these challenges, this paper proposes a multi-level hybrid lightweight water segmentation network, MLHI-Net. First, we design a convolutional module (ORRD) compatible with over-parameterized and redundancy removal techniques based on lightweight design. The over-parameterized convolutional layers enhance the interactive ability of feature representation and context information. The removal of spatial and channel redundancy, in conjunction with interactive reconstruction, serves to simulate attention and enhance the learning ability of waterscape. Then, we design a multi-branch two-layer attention fusion module (MDA), which achieves diverse attention and global optimisation of edge details by connecting spatial attention, channel attention and pixel attention in parallel. thereby guiding feature fusion and solving the problem of receptive field mismatch. This module guides feature fusion and solves the problem of receptive field mismatch. To validate the proposed methodology, a dataset, CityWater, was constructed, with multiple fields and climatic conditions, and a substantial number of experiments were conducted on this and other public datasets. Experimental results show that MLHI-Net outperforms other advanced segmentation networks in Mean Intersection over Union (MIoU) and Pixel Accuracy (PA) on the CityWater and USVInland datasets, with MIoU of 97.86% and PA of 98.92% on the CityWater dataset, and MIoU of 98.12% and PA of 99.10% on the USVInland dataset. Additionally, the network’s computational GLOPS is 13.45 G, and the number of parameters is 46.92 M, which can meet the requirements for real-time detection. The MLHI-Net has been shown to perform well in a range of environments. In addition, it has good generalisation capabilities, providing reliable support to the autonomous system.