Abstract: Highway assets serve as an important component of transportation infrastructure, encompassing highway structures, land used for highways and various ancillary facilities. Addressing the challenge of existing lightweight models struggling to balance accuracy and efficiency in multi-scale detection of various kinds of highway asset facilities, an improved lightweight multi-scale detection model based on YOLOv8n was proposed. Three kinds of representative highway asset facilities, including streetlights, traffic signs and pavement markings were selected as detection objectives. By fusing unmanned aerial vehicle (UAV) field survey data with the VisDrone2019 data, the UAV-HIA dataset was established to enhance data diversity and model robustness. Model improvements included: replacing the backbone network with MobileNetV3-Small to reduce model parameter count and computational complexity; embedding the CBAM attention mechanism in the backbone network to enhance the ability to extract features of small targets; designing the C2iAF feature fusion module based on C2f and iAFF to improve the expression capability of multi-scale features. Experiments demonstrate that the improved model maintains accuracy improvement while significantly reducing computation and parameter quantity, especially achieving better detection performance for small targets. Compared to other existing mainstream models and the newly released YOLO model, the improved model exhibits comprehensive advantages in efficiency, accuracy and adaptability, making it suitable for practical intelligent inspection tasks of highway assets.

Key words: highway engineering; highway asset detection; multi-scale object detection; UAV imagery; YOLOv8n; lightweight model

摘要： 公路路产作为交通基础设施的重要组成部分，涵盖公路结构、公路用地及各类附属设施。针对现有轻量化模型在多类路产设施多尺度检测中难以兼顾精度与效率的问题，基于YOLOv8n提出一种改进轻量化多尺度检测模型。选取路灯、交通标志牌和路面标线3类典型路产设施作为检测目标，通过融合无人机实测数据与VisDrone2019数据构建UAV-HIA数据集，增强数据多样性和模型稳定性。模型改进包括：采用MobileNetV3-Small替换主干网络，降低模型参数量和计算复杂度；在骨干网络中嵌入CBAM注意力机制，增强小目标特征提取能力；基于C2f与iAFF设计C2iAF特征融合模块，提升多尺度特征表达能力。实验表明：改进模型在计算量和参数量显著降低的同时，仍保持精度提升，尤其对小目标的检测效果更优。相较于现有其他主流模型和最新发布的YOLO模型，改进模型在效率、精度和适应性上更具综合优势，适用于实际路产智能巡检任务。

关键词: 道路工程；公路路产检测；多尺度目标检测；无人机影像；YOLOv8n；轻量化模型