This study proposes a method of autonomous navigation UAV for oil and gas pipeline (OGP) dial detection based on the improved YOLOv7 model. The canny edge detection algorithm is applied in identifying the edges of the pipeline, and the Hough transform algorithm is used to detect the pipeline in a straight line. The intelligent UAV P600 is guided to patrol the oil and gas dials (OGD) along the pipeline, and the trained improved YOLOv7-based model is adopted to identify the OGD data. Dial recognition is divided into two stages, that is, dial contour detection and dial reading recognition. For the dial recognition rate (RR), the Levenstein distance, a commonly used method, is introduced, thereby calculating the distance between two character sequences. Meanwhile, an integrated global attention mechanism (GAM) is proposed based on the YOLOv7 model, aiming at extracting more informative features. With this mechanism, the channel and spatial aspects of the features are effectively captured, and the importance of cross-dimensional interactions is increased. By introducing GAM attention mechanism in the backbone and head of YOLOv7, the network’s ability in efficiently extracting depth and primary features is enhanced. ACmix (a hybrid model combining the advantages of self-attentiveness and convolution) is also included, with ACmix module improved. The improved ACmix module has the objectives of enhancing feature extraction capability of backbone network and accelerating network convergence. By substituting 3 × 3 convolutional block with 3 × 3 ACmixBlock and adding a jump connection and a 1 × 1 convolutional structure between the ACmixBlock modules, E-ELAN module in YOLOv7 network is also improved, thus optimizing E-ELAN network, enriching features extracted by E-ELAN network, and reducing inference time of YOLOv7 model. As indicated by comparing the experimental results of the six model algorithms (improved YOLOv7, YOLOv7, YOLOX, YOLOv5, YOLOv6 and Faster R-CNN), the improved YOLOv7 model has higher mAP, faster RR, faster network convergence, and higher IOU. In addition, a generic real dataset, called custom dial reading dataset, is presented. With well-defined evaluation protocol, this dataset allows for a fair comparison of various methods in future work.

中文翻译：

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基于改进YOLOv7的油气管道巡检无人机

本研究提出一种基于改进的YOLOv7模型的自主导航无人机用于油气管道（OGP）刻度盘检测的方法。采用Canny边缘检测算法来识别管道的边缘，采用Hough变换算法来检测管道的直线方向。引导智能无人机P600对管道沿线油气标度（OGD）进行巡检，采用训练好的基于YOLOv7的改进模型对OGD数据进行识别。表盘识别分为两个阶段，即表盘轮廓检测和表盘读数识别。对于拨号识别率（RR），引入了常用的方法Levenstein距离，从而计算两个字符序列之间的距离。同时，基于YOLOv7模型提出了一种集成的全局注意力机制（GAM），旨在提取更多信息量的特征。通过这种机制，可以有效地捕获特征的通道和空间方面，并增加跨维度交互的重要性。通过在YOLOv7的backbone和head中引入GAM注意力机制，增强了网络高效提取深度和主要特征的能力。还包括ACmix（一种结合了自注意力和卷积优点的混合模型），并对ACmix模块进行了改进。改进后的ACmix模块的目的是增强骨干网络的特征提取能力，加速网络收敛。通过用3×3 ACmixBlock替代3×3卷积块，并在ACmixBlock模块之间添加跳跃连接和1×1卷积结构，YOLOv7网络中的E-ELAN模块也得到改进，从而优化了E-ELAN网络，丰富了提取的特征通过E-ELAN网络，减少YOLOv7模型的推理时间。通过对比六种模型算法（改进型YOLOv7、YOLOv7、YOLOX、YOLOv5、YOLOv6和Faster R-CNN）的实验结果可以看出，改进型YOLOv7模型具有更高的mAP、更快的RR、更快的网络收敛和更高的IOU。此外，还提供了一个通用的真实数据集，称为自定义表盘读数数据集。通过明确定义的评估协议，该数据集可以在未来的工作中对各种方法进行公平的比较。