论文标题
改进的深度卷积神经网络基于无人机的自主道路检查计划
An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles
论文作者
论文摘要
人工智能（AI）的进步提供了一个很好的机会来开发自主设备。这项工作的贡献是改进的卷积神经网络（CNN）模型及其在道路上检测道路裂纹，坑洼和黄色车道的实施。黄道检测和跟踪的目的是通过遵循黄色车道来实现无人驾驶汽车（UAV）的自动导航，同时通过WiFi或5G培养基检测和报告道路裂缝和坑洼。自己的数据集的制造是一项忙碌而耗时的任务。使用默认模型和改进的模型创建，标记和训练数据集。这两种模型的性能均在准确性，平均平均精度（MAP）和检测时间方面进行基准测试。在测试阶段，观察到改进模型的性能在准确性和MAP方面更好。改进的模型使用机器人操作系统在无人机中实现，以实时通过UAV前摄像头视觉自动检测道路上的坑洼和裂缝。
Advancements in artificial intelligence (AI) gives a great opportunity to develop an autonomous devices. The contribution of this work is an improved convolutional neural network (CNN) model and its implementation for the detection of road cracks, potholes, and yellow lane in the road. The purpose of yellow lane detection and tracking is to realize autonomous navigation of unmanned aerial vehicle (UAV) by following yellow lane while detecting and reporting the road cracks and potholes to the server through WIFI or 5G medium. The fabrication of own data set is a hectic and time-consuming task. The data set is created, labeled and trained using default and an improved model. The performance of both these models is benchmarked with respect to accuracy, mean average precision (mAP) and detection time. In the testing phase, it was observed that the performance of the improved model is better in respect of accuracy and mAP. The improved model is implemented in UAV using the robot operating system for the autonomous detection of potholes and cracks in roads via UAV front camera vision in real-time.
