Real-Time Detection of Railway Signal Gantries via Improved RT-DETR: Edge Deployment and Cloud Empowerment

The precise and timely extraction of railway signals is crucial for the creation of railway electronic maps. This paper introduces a novel real-time detection approach for dynamically adjusting railway signals, leveraging an enhanced Real-Time DEtection TRansformer (RT-DETR) model. The enhancement involves the integration of a vision Transformer with Dynamically Quantifiable Sampling Attention Mechanism (DQSAM) into the ResNet50 backbone of the RT-DETR framework, thereby enhancing the model's efficiency and accuracy in handling intricate visual tasks. Secondly, an ultra-lightweight and effective Dynamic Grouping upSampler (DyGSample) is inserted into the efficient hybrid encode module as the up-sampling part. This operator can effectively upsample the feature graph without increasing the computational burden, and improve the model resolution and detail capture ability. In addition, in order to solve the problem of deep layer of model network and high operating cost, a new bounding box similarity loss function of rotation intersection over union based on minimum point distance is adopted in this paper, which takes into account all relevant factors of existing loss functions, namely overlapping or non-overlapping regions, center point distance, width and height deviation, and simplifies the calculation process. As a lightweight signal detection model with ultra-fast, high real-time, and high precision, the detection accuracy of this method is improved from 90.21% to 97.45%, which proves the superior performance and effectiveness of the improved real-time dynamic adjustment RT-DETR model in railway signal extraction.