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The primary premise of autonomous railway inspection using unmanned aerial vehicles is achieving autonomous flight along the railway. In our previous work, fitted centerline-based unmanned aerial vehicle (UAV) navigation is proven to be an effective method to guide UAV autonomous flying. However, the empirical parameters utilized in the fitting procedure lacked a theoretical basis and the fitted curves were also not coherent nor smooth. To address these problems, this paper proposes a skeleton detection method, called the dynamic-weight parallel instance and skeleton network, to directly extract the centerlines that can be viewed as skeletons. This multi-task branch network for skeleton detection and instance segmentation can be trained end to end. Our method reformulates a fused loss function with dynamic weights to control the dominant branch. During training, the sum of the weights always remains constant and the branch with a higher weight changes from instance to skeleton gradually. Experiments show that our model yields 93.98% mean average precision (mAP) for instance segmentation, a 51.9% F-measure score (F-score) for skeleton detection, and 60.32% weighted mean metrics for the entire network based on our own railway skeleton and instance dataset which comprises 3235 labeled overhead-view images taken in various environments. Our method can achieve more accurate railway skeletons and is useful to guide the autonomous flight of a UAV in railway inspection.

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