LAUSR

UAV-based automatic railway inspection is expected to have the potential to reform the inspection of railways. In this area, real-time railway scene parsing is quite essential. However, the limited computation resources of the UAV onboard computer pose a huge challenge for the algorithm to juggle a precise prediction with strong timeliness. Concerning this issue, this paper proposes a novel algorithm named deep fully decoupled residual convolutional network, which consists of fully decoupled residual blocks (Non-bottleneck-FDs) to deal with the dilemma between the high demand of real-time and limited resources. The residual block is constructed based on a new convolution which divides the standard convolution into three sequential convolutions to decouple the conventional operational correlations fully. Furthermore, a customized auxiliary line loss (LL) function is proposed to constrain the segmentation of railway and non-railway simultaneously without increasing the computation complexity. The proposed LL can force the predicted railway areas to concentrate in long strip areas precisely and inhibit their appearances in other impossible local areas. Subsequently, an integrated loss backpropagation strategy of the LL and cross-entropy function is presented. A comprehensive set of experiments are conducted for verification. Experiments demonstrate the superior performance of our approach with a more than $2\times $ reduction in parameters and computation cost. Moreover, our approach also has a faster inference speed than the most existing lightweight architectures while providing comparable or higher accuracy. It is proven that our approach can reconcile the precise prediction with strong timeliness for railway scene parsing within the limitation of onboard computers. Besides, the results also imply its highest performance in terms of local details and edges of railway areas.