Detecting deformable linear objects (DLOs), such as floor cables, is essential for safe mobile robot navigation but remains challenging due to oblique viewpoints, thin structures, and limited edge-device resources. Existing DLO segmentation methods are primarily designed for manipulator platforms with fixed top-down views and often require heavy models, limiting their deployment on mobile robots. We formulate a cross-view DLO segmentation problem, where models trained on manipulator-view data must generalize to mobile robot perspectives. To address this, we propose ASC-SW, a lightweight and geometry-aware segmentation framework. The core network, ASCNet, introduces Atrous Strip Convolution, combining directional strip filtering with dilated receptive fields to enhance sensitivity to elongated structures at low computational cost. An Atrous Strip Convolution Spatial Pyramid Pooling module enables multi-scale anisotropic feature aggregation, while a temporal Sliding Window refinement suppresses viewpoint-induced false positives. Evaluated on real-world mobile robot data, ASC-SW achieves 74.1% mIoU at 261 FPS and remains deployable on edge devices.

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