ME-YOLO: A novel real-time detection network for pavement interlayer distress using ground-penetrating radar

Abstract

Interlayer distress detection in asphalt pavement is critical for highway maintenance, as timely identification of pavement distress can ensure operational safety, reliability, and extended service life. However, the problems of feature information loss and the substantial confusable backgrounds significantly hinder detection accuracy. To address these limitations, we propose an enhanced network specifically designed for automated interlayer distress detection named ME-YOLO. Firstly, we design a Multiscale Adaptive Feature Fusion (MAFF) module, which aggregates more scale information by Adaptive Spatial Feature Fusion (ASFF). This design links all feature scales to make discriminative features in each scale propagate directly to subsequent modules, enriching semantic representations and mitigating the risk of feature loss, while leveraging shallow-layer features to strengthen spatial localization. Furthermore, the Efficient Partial Self-Attention (EPSA) module is introduced to suppress background interference in complex environments. Unlike conventional transformers, EPSA adopts partial self-attention operations with multi-path fusion, which can enable the network to acquire global representation capability with low computational overhead. Extensive experiments indicate that the ME-YOLO network outperforms the given state-of-the-art models, including Faster-RCNN, RT-DETR, YOLOv8s, and YOLOv11s, on the interlayer distress dataset. Compared to YOLOv5s, ME-YOLO achieves improvements of 2.2% in mAP_0.5 and 3.5% in mAP_0.5:0.95, while maintaining an inference speed of 6.7 ms per image. The source code will be available at https://github.com/caosenguo/ME-YOLO.