Yang Manman, Blight Andrew, Bhardwaj Hitesh, Shaukat Nabil, Han Linyan, Richardson Robert, Pickering Andrew, Jackson-Mills George, Barber Andrew
School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK.
Sensors (Basel). 2025 Mar 13;25(6):1782. doi: 10.3390/s25061782.
Miniature robots in small-diameter pipelines require efficient and reliable environmental perception for autonomous navigation. In this paper, a tiny machine learning (TinyML)-based resource-efficient pipe feature recognition method is proposed for miniature robots to identify key pipeline features such as elbows, joints, and turns. The method leverages a custom five-layer convolutional neural network (CNN) optimized for deployment on a robot with limited computational and memory resources. Trained on a custom dataset of 4629 images collected under diverse conditions, the model achieved an accuracy of 97.1%. With a peak RAM usage of 195.1 kB, flash usage of 427.9 kB, and an inference time of 1693 ms, the method demonstrates high computational efficiency while ensuring stable performance under challenging conditions through a sliding window smoothing strategy. These results highlight the feasibility of deploying advanced machine learning models on resource-constrained devices, providing a cost-effective solution for autonomous in-pipe exploration and inspection.
小直径管道中的微型机器人在自主导航时需要高效可靠的环境感知能力。本文提出了一种基于微型机器学习（TinyML）的资源高效管道特征识别方法，用于微型机器人识别诸如弯头、接头和转弯等关键管道特征。该方法利用了一个定制的五层卷积神经网络（CNN），该网络针对在具有有限计算和内存资源的机器人上进行部署进行了优化。在一个在不同条件下收集的包含4629张图像的定制数据集上进行训练后，该模型的准确率达到了97.1%。该方法的峰值RAM使用量为195.1 kB，闪存使用量为427.9 kB，推理时间为1693毫秒，通过滑动窗口平滑策略，在确保具有挑战性的条件下性能稳定的同时，展示了高计算效率。这些结果突出了在资源受限设备上部署先进机器学习模型的可行性，为管道内自主探测和检查提供了一种经济高效的解决方案。
