EADI-YOLO: a lightweight and efficient model for rail surface defect detection

As the complexity of railway transportation systems increases, higher demands have been placed on the real-time performance and accuracy of track defect detection, making traditional methods inadequate for intelligent inspection tasks. To address this, this paper proposes a lightweight and efficient improved detection model, EADI-YOLO, based on the YOLOv10n framework, aiming to achieve the optimal balance between detection accuracy, inference speed, and computational cost. The model integrates the C2f-EMAN module into the backbone, combining EMA and NAM attention mechanisms to enhance the representation of key regional features. It adopts the efficient AKConv module to replace standard convolutions, improving multi-scale defect perception while reducing parameter overhead. Furthermore, a DySample dynamic sampling module is introduced in the upsampling stage to better reconstruct feature details and preserve semantic information. An Inner-GIoU regression loss function is designed to optimize bounding box localization accuracy.Experimental results on rail surface defect detection tasks demonstrate the effectiveness of the proposed model. EADI-YOLO achieves a mAP@0.5 of 90.3% and a mAP@0.5:0.95 of 49.1%, with an inference speed of 120 FPS, representing improvements of 2.8%, 6.7%, and 15 FPS over YOLOv10n, respectively. Compared to other mainstream detection models such as RT-DETR and YOLOv13n, EADI-YOLO demonstrates superior detection accuracy while maintaining excellent lightweight and real-time performance. Generalization experiments show that EADI-YOLO maintains an excellent balance between accuracy, speed, and model complexity across different rail defect datasets, making it suitable for intelligent detection of railway track defects.