Accurate and rapid railway track segmentation can assist automatic train
driving and is a key step in early warning to fixed or moving obstacles on the
railway track. However, certain existing algorithms tailored for track
segmentation often struggle to meet the requirements of real-time and
efficiency on resource-constrained edge devices. Considering this challenge, we
propose an edge-enabled real-time railway track segmentation algorithm, which
is optimized to be suitable for edge applications by optimizing the network
structure and quantizing the model after training. Initially, Ghost convolution
is introduced to reduce the complexity of the backbone, thereby achieving the
extraction of key information of the interested region at a lower cost. To
further reduce the model complexity and calculation, a new lightweight
detection head is proposed to achieve the best balance between accuracy and
efficiency. Subsequently, we introduce quantization techniques to map the
model's floating-point weights and activation values into lower bit-width
fixed-point representations, reducing computational demands and memory
footprint, ultimately accelerating the model's inference. Finally, we draw
inspiration from GPU parallel programming principles to expedite the
pre-processing and post-processing stages of the algorithm by doing parallel
processing. The approach is evaluated with public and challenging dataset
RailSem19 and tested on Jetson Nano. Experimental results demonstrate that our
enhanced algorithm achieves an accuracy level of 83.3% while achieving a
real-time inference rate of 25 frames per second when the input size is
480x480, thereby effectively meeting the requirements for real-time and
high-efficiency operation.

我们提出了一种边缘设备启用的实时铁路轨道分段算法，通过优化网络结构和在训练之后对模型进行量化，从而在边缘应用中实现适用性优化。实验结果表明，当输入尺寸为 480x480 时，我们改进的算法在达到 83.3% 的准确率的同时，实现了每秒 25 帧的实时推断速率，从而有效满足了实时和高效率操作的要求。