ID 原文 译文
14985 针对当下球类陪练机器人人机交互能力不足的问题，提出一种基于树莓派和YOLOv5目标检测算法的新型人机交互模式，使机器人实现前进、后退、左移、右移、抛球、踢球6种不同的动作。 In order to solve the problem of insufficient human-computer interaction ability of ball training partner robots,a new human-computer interaction mode based on Raspberry Pi and YOLOv5 algorithm was proposed, which enabled the robot to realize six different actions: forward, backward, left, right,throwing the ball,and kicking the ball.
14986 通过对在3种不同环境(室内、室外晴天、室外阴天)下搜集的人体姿态数据集进行标定、训练后，得到6种姿态在3种环境中测试集上的识别准确率分别为：室内96.33%、室外晴天95%、室外阴天94.3%。 After calibrating and training the data sets collected in three different environments (indoor,outdoor sunny day and outdoor cloudy day),the recognition accuracy of the six poses in the test set under three different environments is 96.33% indoor,95 % outdoor sunny day, and 94.3% outdoor cloudy day,respectively.
14987 相比基于特征匹配和其他利用手势等小目标检测的算法，基于该算法的机器人具有更高的检测速度和准确性，使机器人更加智能化。 Compared with other algorithms based on feature matching and small target detection using gestures, the robot has higher detection speed and accuracy, which makes the robot more intelligent.
14988 针对生活垃圾的高效分类及搬运处理，设计了一款以边缘嵌入式AI设备Jetson Nano为控制器的光电智能小车系统，该系统设计以YOLOv5为目标检测算法，以Pytorch1.8.1为深度学习框架。 Aiming at the efficient classification and handling of domestic waste,this article designed a photoelectric smart car system with the edge embedded AI device Jetson Nano as the controller. The system is designed with YOLOv5 as the target detection algorithm and Pytorch 1.8.1 as the deep learning framework.
14989 使智能小车从指定区域出发，通过自身的光电传感器在指定范围内搜寻垃圾，利用六轴机械臂对垃圾进行分拣并送到指定分类地点。 The system makes the smart car start from the designated location,search for garbage in the designated area through its own photoelectric sensor,identify and classify the garbage, and use the sixaxis robotic arm to sort the garbage and send it to the designated stacking place.
14990 对采集到的5 048张图片(包括5种垃圾类别)进行300次的迭代训练，实验测试结果表明：平均精确度达到91.8%，准确率达到94.5%，召回率达到89.03%。 300 iterations of training were performed on the collected 5 048 pictures and 5 types of garbage.The experimental test results show that the average accuracy reaches 91.8%,the accuracy rate reaches 94.5%,and the recall rate reaches 89.03%.
14991 近年来，如何通过人工智能对人的面部表情进行识别分析成为一个研究热点，利用人工智能可以快速地分析人的面部情绪，并以此为基础进行进一步研究。 In recent years,how to recognize and analyze people's facial expressions through artificial intelligence has become a research hotspot.Using artificial intelligence can quickly analyze people's facial emotions,and further research is carried out on this basis.
14992 在深度学习中，传统的卷积神经网络存在对面部表情特征的提取不充分以及计算机参数量较大的问题，导致分类准确率较低。 In deep learning,the traditional convolutional neural network can not extract facial expression features sufficiently,and the amount of computer parameters is large,which leads to low classification accuracy.
14993 因此，提出了基于VGG16网络的人脸表情识别算法，通过与InceptionV3、InceptionResNetV2、ResNet50等模型实验对比。 Therefore, a facial expression recognition algorithm based on VGG16 neural network is proposed. Compared with the model experiments of InceptionV3, InceptionResNetV2 and ResNet50.
14994 结果表明，VGG16神经网络在FER2013PLUS测试数据集上的识别准确率为79%，准确率比传统的卷积神经网络高。 The results show that the recognition accuracy of VGG16 neural network on FER2013 PLUS test data set is 79%, which is higher than that of traditional convolution neural network.