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With the development of computer technology, fire image processing technology combining computer vision, machine learning, deep learning and other technologies has been widely studied and applied. Aiming at the complex preprocessing process and high false positive rate of traditional image processing methods, this paper proposes a method based on deep convolutional neural network model for fire detection, which reduces complex preprocessing links and integrates the whole fire identification process into one single depth neural network for easy training and optimization. In view of the problem of fire detection caused by similar fire scenes in the identification process, this paper uses the motion characteristics of fire to innovatively propose the combination of fire frame position changes before and after the fire video to eliminate the interference of lights and other similar fire scenes. After comparing many open learning open source frameworks, this paper chooses Caffe framework for training and testing. The experimental results show that the method realizes the recognition and localization of fire images. This method is suitable for different fire scenarios and has good generalization ability and anti-interference ability.

At present, accurate face recognition has been widely used in real scenes, such as monitoring, access control, etc., and recognition only for face attributes is also widely used in entertainment, security and social media. Therefore, the paper is based on deep convolutional neural network MTCNN for face detection and Resnet50 network for face attribute recognition. At the same time, it uses Python language to design and develop a system that implements face attribute recognition application interface. Through data experiments, the system uses a face attribute recognition model based on deep convolutional neural networks to achieve 97.32%, 71.64% and 92.13% accuracy in identifying gender, age and ethnic attributes, respectively. At the same time, the face attribute recognition application system can run stably and has certain application value.

With the rise of deep learning in recent years, it has made major breakthroughs in machine learning fields such as target detection, image classification, speech recognition, and natural language processing. Among them, convolutional neural networks are widely used in deep learning. Since the emergence of VGGNet, neural networks have gradually developed deeper and the network has become deeper. This not only increases the demand for operating memory and running memory, but also greatly increases the amount of computation. The requirements for hardware platform resources are getting higher and higher. Therefore, it is particularly difficult to apply deep learning to embedded platforms. In this paper, by pruning the model, the trained network model is compressed, the unimportant parameters are eliminated, the model is reduced, and the computational complexity of the network is reduced. The deep learning will be applied to the embedded platform.

This paper analyses the hot point about the “Belt and Road” initiative of American mainstream news media and studies the sentiment of related public opinion. Web crawler is used to automatically collect relevant news and filter high-frequency words to get media attention hotspots. An integrated model of automatic summary-convolutional neural network(CNN) is proposed for document-level sentiment analysis. The model firstly extracts the abstraction to remove the interference of non-important data in the original document, then the convolutional neural network is used to analyze the sentence-level sentiment, obtain the document-level emotional score based on the semantic pointing method, and the emotional fluctuation abnormal articles are analyzed twice. Contrastive experiments on real data shows that the automatic summary-CNN integrated document-level sentiment analysis model is superior to the single CNN method in sentiment analysis.

This paper proposed a scheme design of intelligent road card recognition system based on machine vision and deep learning. ARM9 is used as the part of front-end collection system,the image processing algorithm is used to complete the area extraction and segmentation of the road card text from the front-end acquisition image, the deep learning with a convolutional neural network is used to recognize the text in the cloud server, and the speech of the text information is presented to the user. Using the front-end hardware to get the road card images on the high way, the function of our system under CAFFE framework on the server is tested and the results show that the system can broadcast the road card information effectively to the user in real time.

This paper proposes an intelligent automated vending system based on machine vision and deep learning. The embedded ARM9 and various sensors are used to form the front-end acquisition system. The trained convolutional neural network is used to detect and identify the goods dynamically and statically on the server. Then the identifying information will be fed back to the databases, and the databases will integrate all the information. Finally it will determine the order information of customers. This system uses front-end hardware to acquire images on the automated vending counter and test them on the server's Caffe framework, and the result shows that the accuracy of this system can reach 99% in real time.