关键词: 异物入侵检测, YOLOv8n, 自适应扩张卷积, 特征校准, 特征融合

Abstract: In response to the low efficiency of existing magnetic levitation train foreign object intrusion detection systems and the problems of misdetection and missed detection of foreign objects of different sizes, a foreign object detection algorithm based on improved YOLOv8n, named YOLO-RFCL (robust feature calibration lightweight network), is proposed. Adopting the robust feature downsampling (RFD) module to integrate various downsampling features, the model’s feature expression ability is optimized. It introduces frequency adaptive dilation convolution (FADC), uses a modulation mechanism to assign different dilation rates for each pixel, reduces the model’s complexity and computational cost. It establishes a context feature calibration network (CFCN) to improve small target detection accuracy through self-calibration and local detail fusion. It proposes the DEHead (detail-enhanced head) to enhance the model’s representation and generalization capabilities. Experimental results show that on the custom magnetic levitation train foreign object intrusion dataset, YOLO-RFCL improves the precision (P), recall (R), mAP50, and mAP95 by 2.3, 6.6, 4.3, and 2.5 percentage points, respectively, compared to the original algorithm, and reduces the parameter count (Params) by 3.7%. Compared to YOLOv11n, YOLO-RFCL improves precision (P), mAP50, and mAP95 by 3.9, 2.9, and 2.4 percentage points, respectively. The detection speed of YOLO-RFCL reaches 111.8 FPS, validating the effectiveness and accuracy of the algorithm.

Key words: foreign object intrusion detection, YOLOv8n, adaptive extended convolution, feature calibration, feature fusion