1.东北林业大学 土木与交通学院，黑龙江 哈尔滨150000;

路面作为交通基础设施，在长期承受荷载和环境侵蚀作用下易产生裂缝等病害。传统人工巡检方法存在效率低、主观性强、安全风险大等问题，难以满足大规模路面网络的高效养护需求。提出一种轻量级高精度路面裂缝检测算法LMC-YOLO，针对细长裂缝在图像中易出现的特征丢失和检测精度不足问题，对检测网络的主干结构、注意力机制融入方式和轻量化策略进行了系统性优化。算法通过在主干网络中引入轻量化的MobileNetV4（MNV4）结构，利用其高效的通用反转瓶颈模块（UIB），实现了强大特征提取能力与低计算开销的统一；通过将改进的上下文锚点注意力机制（CAA）融入检测网络的颈部部分，在条带卷积基础上引入裂缝形状感知模块，有效增强了对细长裂缝的检测能力；通过精细化的网络设计，模型参数量减少24%，计算量降至4.6GFLOPs。结果表明LMC-YOLO在裂缝检测任务中取得了91.1%的精确率、84.6%的mAP@0.5和70.3%的mAP@0.5:0.95，F1-score达到80.40%，推理速度达到345FPS，在DIOR和DOTA-v2数据集上的交叉验证进一步证实了模型的跨领域迁移能力。该方法成功实现了高精度与高效轻量化的有机结合，为移动端和嵌入式设备上的路面裂缝实时检测提供了切实可行的解决方案。

Road pavements, as critical transportation infrastructure, are prone to cracks and other defects due to long-term load bearing and environmental erosion. Traditional manual inspection methods suffer from

low efficiency, high subjectivity, and significant safety risks, failing to meet the maintenance demands of large-scale pavement networks. This paper proposes LMC-YOLO (Lightweight MobileNetV4 with CAA for YOLO), a lightweight and high-precision pavement crack detection algorithm that addresses feature loss and insufficient accuracy in detecting thin and elongated cracks. The algorithm systematically optimizes the backbone structure, attention mechanisms, and lightweighting strategies of the detection network. By incorporating the lightweight MobileNetV4 with an efficient Universal Inverted Bottleneck (UIB) architecture as the backbone, a balance between powerful feature extraction and low computational overhead is achieved. The improved Context Anchor Attention (CAA) mechanism is integrated into the Neck of the detection network, introducing a shape-aware module based on strip convolution that effectively enhances detection capability for thin elongated cracks. Through refined network design, the model reduces parameter count by 24% and achieves a computational cost of 4.6 GFLOPs. Experimental results demonstrate that LMC-YOLO achieves 91.1% precision, 84.6% mAP@0.5, and 70.3% mAP@0.5:0.95 in crack detection tasks, with an F1-score of 80.40% and an inference speed of 345 FPS. Cross-dataset validation on DIOR and DOTA-v2 further confirms the model's cross-domain transfer capability. This method successfully combines high accuracy with efficient lightweighting, providing a practical solution for real-time pavement crack detection on mobile and embedded devices.