YOLOv11s-CD: An Improved YOLOv11s Method for Catenary Dropper Fault Detection

The catenary dropper (CD) fault detection is an important technical means to ensure the train current collection quality and operational safety. The existing YOLO detection algorithms need improvement in terms of accuracy, especially in the detection of small objects. To address the problem, this article proposes a catenary dropper fault detection model based on improved YOLOv11s, named YOLOv11s-CD. First, a four detection head structure DASFFHead is designed to achieve multi-scale feature fusion, by integrating an small object detection layer into the neck network and combining a dynamic adaptive spatial feature fusion module DASFF. Subsequently, the SEAM attention mechanism is embedded in the neck network layer to extract more small objects features in occluded areas. Additionally, combining the Inner-IoU and CIoU method, the InnerCIoU loss function is designed to enhance the small object detection ability. Finally, The effectiveness and accuracy of the proposed model is validated on the dataset, which is processed by the optimized contrast limited adaptive histogram equalization (CLAHE) algorithm to enhance the contrast and clarity of the small object defects. Experimental results show that the proposed YOLOv11s-CD has superior performance compared with several other YOLO algorithms, whose mAP@0.5 has increased to 92.3% and AP of small object detection has significantly increased to 91.3%.