College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
Sensors (Basel). 2022 Jun 9;22(12):4364. doi: 10.3390/s22124364.
To address power transmission line (PTL) traversing complex environments leading to data collection being difficult and costly, we propose a novel auto-synthesis dataset approach for fitting recognition using prior series data. The approach mainly includes three steps: (1) formulates synthesis rules by the prior series data; (2) renders 2D images based on the synthesis rules utilizing advanced virtual 3D techniques; (3) generates the synthetic dataset with images and annotations obtained by processing images using the OpenCV. The trained model using the synthetic dataset was tested by the real dataset (including images and annotations) with a mean average precision (mAP) of 0.98, verifying the feasibility and effectiveness of the proposed approach. The recognition accuracy by the test is comparable with training by real samples and the cost is greatly reduced to generate synthetic datasets. The proposed approach improves the efficiency of establishing a dataset, providing a training data basis for deep learning (DL) of fitting recognition.
为了解决输电线路（PTL）穿越复杂环境导致数据采集困难且成本高的问题，我们提出了一种新的基于先验序列数据的自动合成数据集拟合识别方法。该方法主要包括三个步骤：（1）利用先验序列数据制定合成规则；（2）利用先进的虚拟 3D 技术根据合成规则生成 2D 图像；（3）利用 OpenCV 对图像进行处理，生成包含图像和标注的合成数据集。使用真实数据集（包括图像和标注）对使用合成数据集训练的模型进行测试，平均准确率（mAP）为 0.98，验证了所提出方法的可行性和有效性。测试的识别准确率与使用真实样本进行训练相当，而生成合成数据集的成本大大降低。该方法提高了建立数据集的效率，为拟合识别的深度学习（DL）提供了训练数据基础。
