Liu Ruihao, Shao Zhongxi, Sun Qiang, Yu Zhenzhong
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
Hefei Intelligent Robot Institute, Hefei 230601, China.
Sensors (Basel). 2024 Nov 26;24(23):7557. doi: 10.3390/s24237557.
Detecting defects in complex urban sewer scenes is crucial for urban underground structure health monitoring. However, most image-based sewer defect detection models are complex, have high resource consumption, and fail to provide detailed damage information. To increase defect detection efficiency, visualize pipelines, and enable deployment on edge devices, this paper proposes a computer vision-based robotic defect detection framework for sewers. The framework encompasses positioning, defect detection, model deployment, 3D reconstruction, and the measurement of realistic pipelines. A lightweight Sewer-YOLO-Slim model is introduced, which reconstructs the YOLOv7-tiny network by adjusting its backbone, neck, and head. Channel pruning is applied to further reduce the model's complexity. Additionally, a multiview reconstruction technique is employed to build a 3D model of the pipeline from images captured by the sewer robot, allowing for accurate measurements. The Sewer-YOLO-Slim model achieves reductions of 60.2%, 60.0%, and 65.9% in model size, parameters, and floating-point operations (FLOPs), respectively, while improving the mean average precision (mAP) by 1.5%, reaching 93.5%. Notably, the pruned model is only 4.9 MB in size. Comprehensive comparisons and analyses are conducted with 12 mainstream detection algorithms to validate the superiority of the proposed model. The model is deployed on edge devices with the aid of TensorRT for acceleration, and the detection speed reaches 15.3 ms per image. For a real section of the pipeline, the maximum measurement error of the 3D reconstruction model is 0.57 m. These results indicate that the proposed sewer inspection framework is effective, with the detection model exhibiting advanced performance in terms of accuracy, low computational demand, and real-time capability. The 3D modeling approach offers valuable insights for underground pipeline data visualization and defect measurement.
检测复杂城市下水道场景中的缺陷对于城市地下结构健康监测至关重要。然而，大多数基于图像的下水道缺陷检测模型复杂，资源消耗高，且无法提供详细的损坏信息。为了提高缺陷检测效率、可视化管道并实现边缘设备上的部署，本文提出了一种基于计算机视觉的下水道机器人缺陷检测框架。该框架包括定位、缺陷检测、模型部署、三维重建以及真实管道的测量。引入了轻量级的Sewer-YOLO-Slim模型，通过调整其主干、颈部和头部来重构YOLOv7-tiny网络。应用通道剪枝进一步降低模型的复杂度。此外，采用多视图重建技术，根据下水道机器人拍摄的图像构建管道的三维模型，以便进行精确测量。Sewer-YOLO-Slim模型在模型大小、参数和浮点运算（FLOPs）方面分别减少了60.2%、60.0%和65.9%，同时将平均精度均值（mAP）提高了1.5%，达到93.5%。值得注意的是，剪枝后的模型大小仅为4.9MB。与12种主流检测算法进行了全面的比较和分析，以验证所提模型的优越性。该模型借助TensorRT在边缘设备上进行加速部署，检测速度达到每张图像15.3毫秒。对于一段真实的管道，三维重建模型的最大测量误差为0.57米。这些结果表明，所提的下水道检测框架是有效的，检测模型在准确性、低计算需求和实时能力方面表现出先进的性能。三维建模方法为地下管道数据可视化和缺陷测量提供了有价值的见解。
