Abstract: To address the issues of inconvenient operation,low efficiency,and difficulty in managing recognition data during the manual identification of substation layout drawings,this paper proposes a morphology-based large-size drawing segmentation method and a text-image multimodal fusion drawing symbol detection method.Combined with post-processing methods for symbol detection,this forms a detectable and adaptable approach to large-size layout drawing symbol detection that can be generalized to other fields.The text-image multimodal fusion drawing symbol detection model is improved upon the open-set object detection model YOLO-World,by introducing the CTCM,SOFEM,and CJFFM.These enhancements significantly improve the model's performance in symbol recognition.Using the proposed methods,the detection of symbols in actual high-speed railway traction substation general layout drawings dataset is achieved.Compared to the original model,the proposed improved model,while maintaining a similar level of complexity,reaches an average precision of 97.5% for symbol recognition,with mAP@50:95 and mAP@90 increasing by 1.1% and 3.0%,respectively.

Key words: Text-image multimodal, Feature fusion, Attention mechanism, Small object detection, Diagram symbol recognition