Key Technologies Research On Indoor Scene Recognition Of Mobile Service Robot

Indoor scene recognition is the basis of mobile service robot to provide high quality service for people,and it is also one of the research hotspots of machine vision.Indoor scene recognition mainly includes layout estimation,scene classification and target detection.Because layout estimation is often affected by indoor complex environment,light and shadow,and clutter,it is the most difficult task in indoor scene recognition.At the same time,indoor scene classification is more challenging than outdoor scene classification due to the high complexity of indoor scene.Accordingly,this paper carries out research on the key technologies of indoor scene recognition of mobile service robot.The main research contents are as follows:1.In order to simplify the network structure and improve the efficiency of layout estimation,this paper proposes a real-time layout estimation method based on improved lightweight network.This method uses a lightweight encoding and decoding network to obtain the segmentation layout estimation directly from end-to-end.Aiming at the problem of low feature utilization of previous joint learning methods,a simplified joint learning module is used to simplify the joint learning network and improve feature utilization.In order to improve the stability of network training,segmentation semantic transfer is used to solve the imbalance of positive and negative labels and layout types of data sets.On LSUN and Hedau datasets,the results were evaluated using pixel error indicators.Experiments show that the proposed segmentation layout estimation method can obtain layout estimation quickly and accurately.2.In order to improve the accuracy of layout estimation,this paper proposes a stepwise training edge layout estimation method.The rough edge layout is obtained by edge layout estimation network.Aiming at the problem of complex network structure of previous joint learning methods,a stepwise joint learning method is used to simplify the network structure.Aiming at the imbalance of positive and negative labels in the edge layout training set,a stepwise semantic transfer method is used to improve the stability of training.Then a simplified rough edge layout optimization method is used to improve the accuracy and efficiency of layout estimation.On LSUN and Hedau datasets,the results were evaluated using pixel error and corner error indicators.Experiments show that the proposed method can obtain more accurate layout estimation.3.In order to improve the accuracy of indoor scene classification,this paper uses convolutional neural network to achieve end-to-end indoor scene classification.In order to alleviate the problems caused by too small data set,this paper adopts the training method of transfer learning to initialize part of the weight of the network in this paper by using the network state pre-trained on the large data set to prevent over-fitting due to too few samples.On LSUN data set,classification accuracy index was used to evaluate.The results show that the classification effect of the proposed method is better.4.Based on the research results of this paper,Py Qt5 and Qt Designer are used to design an indoor scene recognition software to achieve segmentation layout estimation,edge layout estimation and indoor scene classification.