A Lightweight Multi-Scale Feature Fusion Signal Detection Model for Metro Computer Interlocking Systems

Metro Computer Interlocking Systems are crucial for ensuring the safe and efficient operation of rail transit. However, existing vision-based signal detection methods face challenges including small target sizes, high target density, low image resolution, and the need for deployment on resource-constrained devices. To address these issues, this paper proposes a two-stage lightweight signal detection framework for Metro Computer Interlocking Systems. First, based on YOLOv8, a small object detection layer together with feature fusion modules is introduced to form the YOLOv8-SFF architecture. A Scale Sequence Feature Fusion (SSFF) module is added to adjust the resolution of feature maps and retain critical fine-grained information, enhancing the detection of small visual signals. A Triple Feature Encoding (TFE) module is designed to enhance the recognition of dense small signals while replacing some traditional feature concatenation and upsampling operations, yielding a more compact network. Second, to enable practical deployment, a joint optimization strategy combining Layer-Adaptive Magnitude-based Pruning (LAMP) and Channel-wise Knowledge Distillation (CWD) is applied, in which the unpruned YOLOv8-SFF serves as the teacher and the pruned model serves as the student. In addition, an automatic annotation subsystem based on digital image processing is developed, leveraging color and morphological features to generate high-quality labels. Experimental results show that YOLOv8-SFF achieves a mean average precision (mAP@50) of 98.7% , improving mAP@50 by 11.3 percentage points and recall by 22.1 percentage points over the original YOLOv8. After joint pruning and distillation, the final compact model retains 98.0% mAP while reducing the parameter count by 86.2% and the model size to 1.1 MB, making it well suited for real-time deployment in resource-constrained metro dispatching systems.