Research On Outdoor Scene Recognition Based On Deep Learning

Scene recognition is a very important topic in the field of machine vision.With the development of deep learning,scene recognition is gradually combined with deep learning in the fields of drones,driverless,security monitoring,etc.,and has achieved good recognition results.Scene recognition based on deep learning has also become a key research direction in the field of scene recognition.Scene recognition can be divided into indoor scene recognition and outdoor scene recognition.Outdoor scenes are generally composed of open background information,and the recognition difficulty is more difficult than indoor scene recognition.For drones,driverless cars and outdoor robots,it is important to sense and identify the surrounding environment.Therefore,it is important to study the outdoor scene recognition problem based on deep learning.The main research work of this paper is as follows:(1)The outdoor scene is composed of a large amount of background information,and the recognition difficulty is high.In order to meet the research requirements of scene recognition,this paper adopts the deep network model pre-trained on the ImageNet dataset.For the self-built dataset and ImageNet dataset category differences,the GoogleNet Inception V3 network model based on the pre-training model is used,and the learning rate setting problem when the network model is optimized is discussed.(2)The learning rate setting in deep learning backpropagation will cause the network model to converge too slowly,prolong the training time of the network,and the excessive learning rate will lead to divergence.To solve this problem,this paper proposes a The loss function is related to the method of dynamically adjusting the learning rate based on the computational verb theory to optimize the network model.In order to describe the relationship between the loss function and the learning rate,this paper introduces the relationship formula of the step factor and error in the Least Mean Square(LMS)adaptive filtering algorithm of the steepest descent algorithm,and uses the relational formula as the learning rate.The attenuation formula trains the network model.Then the computational verb is added to the relationship between learning rate and loss function.The learning verb similarity is used to derive the learning rate for each round of optimization,parameter adjustment,optimization of the network model,and then a smaller loss function to improve recognition.effect.(3)For the deep learning network model,the description of the local details of the low-level features is relatively weak.This paper proposes a scene recognition method based on the fusion of traditional features and depth features of dColorSIFT.The dColorSIFT algorithm is used to extract the low-level features,and the extracted features are encoded and pooled by the word bag model and the spatial pyramid model in turn,and then merged with the features extracted by the GoogLeNet InceptionV3 network model improved by the computational verbs,and then classified by the SVM classifier.Identification.This method is used to compensate for the shortcomings of the low-level feature local detail description extracted by the deep learning network model,and improve the scene recognition accuracy.