基于卷积神经网络的仿人机器人视觉识别系统设计  

Design of Visual Recognition System for Humanoid Robot based on Convolutional Neural

作　　者：吕增城 郑曜东 LYU Zeng-cheng;ZHENG Yao-dong(Suzhou Institute of Technology,Jiangsu University of Science and Technology,Zhangjiagang 215600,Jiangsu,China)

机构地区：[1]江苏科技大学苏州理工学院,江苏张家港215600

出　　处：《机械研究与应用》2025年第1期106-109,共4页Mechanical Research & Application

摘　　要：为了提高仿人机器人在现实应用中的发展,针对仿人机器人巡线任务,设计了一种准确的识别方法。该方法将传统的卷积操作分解为深度卷积和逐点卷积,大幅度降低了模型的参数量,并引入注意力机制来提高模型识别的准确率。首先对图像数据进行预处理,剔除噪声点的干扰;然后将处理后的图像数据输入到改进的卷积神经网络模型中,通过深度卷积和逐点卷积的组合更好地捕捉图像中的特征信息;再引入有助于提升模型对关键区域注意程度的注意力机制,以进一步提高模型的准确性。该方法已在江苏省大学生机器人大赛中得到验证,测试结果表明:在巡线任务中,仿人机器人可以完成直走、左转、右转三类情况的自主识别,平均正确识别率可达95.86%,运行可靠。这些研究成果对于进一步推动仿人机器人在实际应用中的发展具有重要意义。This paper aims to explore the application of convolutional neural networks in line patrol tasks of humanoid robots.By dividing the traditional convolution operation into deep convolution and point-by-point convolution,the number of parameters of the model is greatly reduced,and attention mechanism is introduced to improve the accuracy of the track recognition model.Firstly,the image data is preprocessed to remove the interference of noise points,and then input into the improved convolutional neural network model.Through the combination of deep convolution and point-by-point convolution,the feature information in the image can be better captured.The introduction of the attention mechanism helps to enhance the attention of the network to the key areas and further improve the accuracy of the patrol.This method has been verified in the Jiangsu University Student Robot Competition and achieved good results.Compared with the traditional methods,this method shows higher accuracy and stability in the patrol task,it can autonomously identify three cases of going straight,turning left,and turning right;the average correct recognition rate can reach 95.86%,and the operation is reliable.These research results are of great significance to further promote the development of humanoid robots in practical applications.

关 键 词：仿人机器人 MobileNetV2 CBAM注意力机制 巡线识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]