It is challenging to distinguish the defective areas using infrared thermography to automatically analyze external post-tensioned tendon duct grouting defects. To achieve efficient and stable automated detection, a lightweight real-time grouting defects detection method based on YOLO deep learning is proposed. Firstly, the Cutpaste data augmentation method was used to effectively alleviate the problem of overfitting. Then, the C3Ghost module was introduced into the neck network, and the number of channels in the network layers was adjusted to 50 % of those in the YOLOv5s model, reducing the number of parameters and computational resources. Finally, the SGD optimizer and GIOU loss function, as well as the Sim attention module, were used to improve detection accuracy. Based on instance analysis and comparison, this method achieves mAP@0.5 of 96.9 % and detection speed of 66FPS. Compared with YOLOv5s, it reduces the number of parameters by 79 % and FLOPs by 77 %.

中文翻译：

---

用于通过红外热成像技术检测外部后张式管道中灌浆缺陷的实时轻量级 YOLO 模型


使用红外热成像自动分析外部后张筋管灌浆缺损来区分缺陷区域是一项挑战。为了实现高效稳定的自动化检测，提出了一种基于 YOLO 深度学习的轻量级实时注浆缺陷检测方法。首先，采用 Cutpaste 数据增广方法有效缓解过拟合问题;然后，将 C3Ghost 模块引入 neck 网络，并将网络层中的通道数调整为 YOLOv5s 模型中的 50 %，减少了参数数量和计算资源。最后，使用 SGD 优化器和 GIOU 损失函数以及 Sim 注意力模块来提高检测精度。通过实例分析对比，该方法的 mAP@0.5 率达到 96.9%，检测速度达到 66FPS。与 YOLOv5s 相比，它减少了 79% 的参数数量和 77% 的 FLOPs。