Abstract
The target detection algorithms have the problems of low detection accuracy and susceptibility to occlusion in existing smart cities. In response to this phenomenon, this paper presents an algorithm for target detection in a smart city combined with depth learning and feature extraction. It proposes an adaptive strategy is introduced to optimize the algorithm search windows based on the traditional SSD algorithm, which according to the target operating conditions change, strengthening the algorithm to enhance the accuracy of the objective function which is combined with the weighted correlation feature fusion method, and this method is a combination of appearance depth features and depth features. Experimental results show that this algorithm has a better antiblocking ability and detection accuracy compared with the conventional SSD algorithms. In addition, it has better stability in a changing environment.
Highlights
The concept of a smart city originated from the idea of smart earth proposed by IBM in 2008
From the perspective of the depth model, it can be broadly classified into a target detection algorithm based on CNN and a target detection algorithm based on SAE [5,6,7]
In order to improve the classification ability of the features of the algorithm, we did the following optimization; like in the feature fusion method of weighted correlation, we combined with the appearance depth feature and the motion depth feature to improve the accuracy of the objective function and perform experiments in different complexity image environments
Summary
The concept of a smart city originated from the idea of smart earth proposed by IBM in 2008. In order to improve the classification ability of the features of the algorithm, we did the following optimization; like in the feature fusion method of weighted correlation, we combined with the appearance depth feature and the motion depth feature to improve the accuracy of the objective function and perform experiments in different complexity image environments. This algorithm is more time-consuming than the traditional SSD algorithm. It has good practical value in the development of a smart city
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