Research on Positioning and Navigation of USV Based on Lidar

Zha, Qing; Huang, Y.

doi:10.1007/978-981-16-6328-4_71

Cited by 2 publications

(1 citation statement)

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“…In addition, for some areas without obvious structure and lacking obvious water shoreline, the detection of the river surface can provide information about the drivable area, thus can improve the active safety function of the unmanned boat navigation system. Water shoreline detection helps to analyze and judge the specific navigation information of inland rivers, thus assisting and ensuring the safe navigation behavior of unmanned boats [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Improved PSPNet-based water shoreline detection in complex inland river scenarios

Yin

Guo²,

Deng³

et al. 2022

Complex Intell. Syst.

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The water shoreline is essential for unmanned surface vessels (USVs) to navigate autonomously. Many existing traditional water shoreline detections approaches not only fail to overcome the effects of water reflections, image inversions, and other factors but are also unsuitable for water shoreline detection in a variety of weather conditions and in complex inland river scenarios. Therefore, we propose a water shoreline detection approach based on an enhanced Pyramid Scene Parsing Network (PSPNet). We introduce a migration learning approach to the PSPNet feature backbone extraction network Resnet50 to improve training efficiency and add a Convolutional Block Attention Module (CBAM) attention mechanism module to improve the robustness of training. In addition, the pyramid pooling module adds the branch of the atrous convolution module. Finally, the waterfront segmentation map is processed by the Canny edge detection method, which detects the water shorelines. For the network's training and validation, we use the USVInland dataset, the world's first urban inland driverless dataset. The experimental results show that the segmentation accuracy MIou of this paper is 96.87% and Accuracy is 98.41, which are higher than some mainstream algorithms. It is capable of detecting water shorelines accurately in a variety of interior river situations.

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Section: Introductionmentioning

confidence: 99%