Prediction Model of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network

Object detection in public spaces on urban streets has always been an important research topic in the field of computer vision networks. Due to the complex and changeable scene in the prediction of public space art design indicators, there are still problems in the research of target detection algorithms in practical applications. Based on the DCNN, this paper studies the accurate detection algorithm and implementation of urban streets in complex scenes. This paper uses the characteristics of DCNN coding to collect and compress data at the same time, studies the prediction module of urban street saliency detection algorithm, and combines saliency map to determine the saliency of urban street art design indicators in the measurement domain. The experimental method can greatly shorten the index prediction scan time and solve the problems of high window calibration redundancy and long positioning time in index prediction. The experimental results show that the proposed method combining urban street mask and public space feature information can reduce other interference information, the average accuracy of target detection is increased by 0.398, and the error is reduced to 3.12%, which significantly promotes urban streets and improves recognition accuracy.