改进Center-Net网络的自主喷涂机器人室内窗户检测

Indoor window detection of autonomous spraying robot based on improved CenterNet network

室内自主喷涂机器人可以实现室内墙面喷涂的自动化以此提升喷涂的效率,减少人力物力的投入。而基于计算机视觉的室内窗户检测算法则是该机器人的关键技术。对于室内窗户检测,由于环境光照、窗户形状和窗户透光属性的存在,传统方法无法得到较好的效果。针对此问题,设计一种基于深度学习的室内窗户检测算法。该算法主要对中心点网络(CenterNet)的特征提取网络进行修改,减少部分卷积操作,使用Ghost模块替换原始的卷积模块,降低特征冗余,并引入注意力机制,让网络尽可能表达重要信息。实验结果表明,改进的CenterNet在不损失网络精度的前提下,大幅度提高了网络的运算速度,使得该检测算法即使在机器人端的嵌入式系统上也可以达到实时检测的效果。

An indoor autonomous spraying robot can realize the automation of indoor wall spraying to improve the efficiency of spraying and reduce the investment of manpower and material resources.The indoor window detection algorithm based on computer vision is the key technology of the robot.For indoor window detection,traditional methods cannot obtain good results owing to the actual scene’s requirements for recognition speed and accuracy as well as the presence of lighting in the environment,shape of the window,and light transmission properties of the window.To solve this problem,an indoor window detection algorithm based on deep learning is designed.This algorithm mainly modifies the backbone feature extraction of the CenterNet network,reduces part of the convolution operation,replaces the original convolution module with ghost block,reduces the redundancy feature,and introduces an attention mechanism to keep the network under a limited number of parameters that express important information as much as possible.The experimental results show that the improved CenterNet algorithm greatly improves the operation speed of the network without losing the accuracy of the network so that the network can achieve a real-time detection effect even on the embedded system of the robot.