Aiming to solve the challenges of low detection accuracy, poor anti-interference ability, and slow detection speed in the traditional tunnel lining defect detection methods, a novel deep learning-based model, named You Only Look Once network v4 enhanced by EfficientNet and depthwise separable convolution (DSC; YOLOv4-ED), is proposed. In the YOLOv4-ED, EfficientNet is used as the backbone to improve the identification accuracy of indistinguishable defect targets in complex tunnel background and light conditions. Furthermore, DSC block is introduced to reduce the storage space of the model and thereby enhance the detection efficiency. The experimental results indicate that the mean average precision, F1 score, Model Size, and FPS of YOLOv4-ED are 81.84%, 81.99%, 49.3 MB, and 43.5 f/s, respectively, which is superior to the comparison models in both detection accuracy and efficiency. Based on robust and cost-effective YOLOv4-ED, a tunnel lining defect detection platform (TLDDP) with the capacity of automated inspection of various lining defects (i.e., water leakage, crack, rebar-exposed) is built. The established TLDDP can realize the high-precision and automatic detection of multiple tunnel lining defects under different lighting and complex background conditions of the practical in-service tunnel.

中文翻译：

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基于增强型 You Only Look Once 网络的隧道衬砌多缺陷自动检测方法

针对传统隧道衬砌缺陷检测方法检测精度低、抗干扰能力差、检测速度慢等挑战，提出了一种基于深度学习的新型模型，名为You Only Look Once network v4，由EfficientNet增强，深度可分离提出了卷积（DSC；YOLOv4-ED）。在YOLOv4-ED中，以EfficientNet为骨干，提高复杂隧道背景和光照条件下无法区分的缺陷目标的识别精度。此外，还引入了 DSC 块，以减少模型的存储空间，从而提高检测效率。实验结果表明，平均精度、F 1 分数、模型大小和FPSYOLOv4-ED 分别为 81.84%、81.99%、49.3 MB 和 43.5 f/s，在检测精度和效率上均优于对比模型。基于强大且经济高效的YOLOv4-ED，构建了隧道衬砌缺陷检测平台（TLDDP），具有自动检测各种衬砌缺陷（即漏水、裂缝、钢筋暴露）的能力。所建立的TLDDP可以实现实际在役隧道不同光照和复杂背景条件下多处隧道衬砌缺陷的高精度自动检测。