重庆大学学报2026，Vol.49Issue(6)：59-70,12.DOI:10.11835/j.issn.1000-582X.2026.06.006
基于深度学习的铁路计算机联锁界面文本定位与识别方法研究
Deep learning-based text location and recognition for railway computer interlocking interfaces
摘要
Abstract
To address the low efficiency and accuracy of manual testing in railway computer interlocking systems,this study proposes a deep learning-based method for text localization and recognition in interlocking interface images.First,a text localization model based on the connectionist text proposal network(CTPN)is developed.By comparing multiple backbone networks(ResNet50,AlexNet,ZF and VGG16),VGG16 is selected as the feature extractor to enhance high-level semantic representation and improve the detection of small text regions.Second,the generalization ability and robustness of the CTPN model are improved through performance comparison with common object detection models and the incorporation of dropout.A projection-based segmentation method,combining horizontal and vertical projections,is further employed to address text adhesion issues in the interface.Finally,an improved AlexNet model is used for text recognition.Experimental results on a railway interlocking interface dataset in the TensorFlow environment show that the proposed method achieves a localization accuracy of 87.98%,a recall of 73.33%,and an F-score of 80.39%,while the recognition accuracy reaches 89%.These results demonstrate that the proposed approach can effectively locate and recognize interface text,providing reliable data support for automated routing and test result analysis in interlocking system testing.关键词
文本定位/文本识别/CTPN/计算机联锁上位机界面/AlexNetKey words
text location/text recognition/CTPN/railway computer interlocking interface/AlexNet分类
信息技术与安全科学引用本文复制引用
何涛,冀毅..基于深度学习的铁路计算机联锁界面文本定位与识别方法研究[J].重庆大学学报,2026,49(6):59-70,12.基金项目
甘肃省科技计划项目(20CX9JA125,20JR5RA407).Supported by Gansu Province Science and Technology Planning Project(20CX9JA125,20JR5RA407). （20CX9JA125,20JR5RA407）
