Yi Bing, Long Qing, Liu Haiqiao, Gong Zichao, Yu Jun
School of Materials and Chemical Engineering, Hunan Institute of Engineering, 411104, Hunan, China.
Graduate School of Hunan University of Engineering, 411104, Hunan, China.
Heliyon. 2024 Jul 17;10(14):e34782. doi: 10.1016/j.heliyon.2024.e34782. eCollection 2024 Jul 30.
To address the issue of detecting complex-shaped cracks that rely on manual, which may result in high costs and low efficiency, this paper proposed a lightweight ground crack rapid detection method based on semantic enhancement. Firstly, the introduction of the Context Guided Block module enhanced the YOLOv8 backbone network, improving its feature extraction capability. Next, the incorporation of GSConv and VoV-GSCSP was introduced to construct a lightweight yet efficient neck network, facilitating the effective fusion of information from multiple feature maps. Finally, the detection head achieved more precise target localization by optimizing the probability around the labels. The proposed method was validated through experiments on the public dataset RDD-2022. The experimental results demonstrate that our method effectively detects cracks. Compared to YOLOv8, the model parameters have been reduced by 73.5 %, while accuracy, F1 score, and FPS have improved by 6.6 %, 4.3 %, and 116, respectively. Therefore, our proposed method is more lightweight and holds significant application value.
针对依靠人工检测复杂形状裂缝可能导致成本高、效率低的问题，本文提出了一种基于语义增强的轻量级地面裂缝快速检测方法。首先，引入上下文引导模块增强YOLOv8主干网络，提高其特征提取能力。其次，引入GSConv和VoV-GSCSP构建轻量级且高效的颈部网络，便于多特征图信息的有效融合。最后，检测头通过优化标签周围的概率实现更精确的目标定位。所提方法通过在公开数据集RDD-2022上进行实验验证。实验结果表明，该方法能有效检测裂缝。与YOLOv8相比，模型参数减少了73.5%，而准确率、F1分数和每秒帧数分别提高了6.6%、4.3%和116。因此，本文提出的方法更轻量级，具有重要的应用价值。
