列车前方铁路轨道区域的检测是列车主动防撞技术的关键环节，现有的铁路区域分割方法多用于简单情景下的轨道检测，难以应对实际运行中的铁路道岔等复杂场景。该文提出了一种面向铁路道岔情景下的列车轨道区域检测方法，解决了现有技术在铁路道岔下难以检测列车实际运行区域的问题。首先，设计了一种基于信息融合的铁路轨道区域分割模型，针对铁路左右钢轨之间难以匹配的问题，对铁路区域和钢轨进行分割并利用其分割结果进行钢轨匹配。其次，设计了一种基于反向透视变换的铁路区域重建方法，通过保留钢轨的关键点来重建铁路区域，同时使用基于分组卷积的铁路道岔分类模型对道岔方向进行识别。实验结果表明，提出的方法在复杂环境下可达到较高的精度，像素准确率可达98.67%，平均交并比可达98.12%，具有在列车上应用的潜力。
The detection of the railway track area in front of the train is a key link in active train collision avoidance technology. The existing railway area segmentation methods are mostly used for track detection in simple scenarios, posing challenges when confronted with complex scenarios such as railway switches in actual operation. We propose a method for detecting railway track areas for railway switches, which solves the problem that existing technology encounters difficulty in detecting the actual running area of trains under railway switches. First, a railway track area segmentation model based on information fusion is proposed. Aiming at the problem of difficulty in matching the left and right rails of the railway, the railway area and the rails are segmented and the segmentation results are used for rail matching. Second, a railway area reconstruction method based on inverse perspective transformation is designed to reconstruct the railway area by preserving the key points of the rails. Meanwhile, a railway switch classification model based on grouped convolution is used to identify the switch direction. Experimental results show that the proposed method achieves high accuracy in complex environments, with pixel accuracy (PA) index of 98.67% and Mean Intersection over Union (MioU) index of 98.12%, showcasing its potential applicability to trains.
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