# A Multidimensional Data Collection and Edge Computing Analysis Method
###### Abstract
By concentrating on image sensors and a select few other sensors as data calibration functions, the amalgamation of deep learning and sensor fusion significantly bolsters the accuracy of parameter extraction.The heightened precision in parameter extraction stems from the inherent framework of deep learning algorithms. These algorithms progressively supersede traditional manual feature labeling through autonomous feature learning by convolutional neural networks (CNN). The regional CNN (R-CNN) is one of the simplest two-order algorithmic models. The core idea of this model is to select a predefined region by means of round robin, and use the CNN network for feature extraction and classification [19]. Subsequently, the SPPNet network emerged to improve the characteristics of the existing R-CNN model by employing convolutional kernels of different sizes to perform feature extraction with different weights [20]. When we use different convolutional kernels to extract features to the pooling layer, we have to classify the feature values. However, the rotation time of the pooling layer is long, so a method that uses labelled feature values of interest at the pooling layer is used to classify the feature values better. This is a Fast R-CNN network [21; 22]. Also, the core feature of this method is to reduce the generation time of candidate frames. This network has achieved more than 80% accuracy in various neural network competitions. Although, the two-order algorithm models have made great progress in terms of time, feature extraction, etc., they are more computationally intensive, meaning in the early stage that the accuracy of these models is still not as good as it should be. In order to improve the accuracy of these models, many researchers have proposed single-order algorithm models. Among the models with better accuracy are YOLO [23], SSD [24], RefineDet [25], and EfficientDet [26]. Among these models, the iterative update speed of YOLO is significantly higher than that of other algorithmic models [27]. And for edge computing type hardware platforms, it may be necessary to compress and prune the YOLO model [28]. Effective porting of edge computing platforms is achieved through various compressions of the model. The edge computing platform can effectively complete the algorithm model running in real time. In this study, we introduce an enhanced target detection model to address the challenge of insufficient accuracy in detecting targets within the intricate settings of public spaces. Leveraging the recent lightweight target detection model YOLOv4-tiny as our foundation, we incorporate the U-Net convolutional neural network to conduct precision-enhancing training on the acquired dataset. Subsequently, this model is fine-tuned for public space scenarios, building upon the YOLOv4-tiny architecture. Our research entails the deployment of sensors and edge computing networks within a residential area in Beijing's Fengtai district, enabling the comprehensive collection of data to delve into public space usage patterns. Real-time data processing is seamlessly executed on the platform at the edge of the data terminal, thereby alleviating the necessity of uploading data to the cloud. Finally, employing data spanning from March 2022 to April 2022, encompassing a month, we conduct an in-depth analysis of public space utilization using this system's model. Our contributions in this paper can be summarized as follows. * A nimble wireless communication hardware platform tailored for public spaces is developed, featuring a diverse array of sensors. These sensors encompass image acquisition, temperature measurement, and location detection capabilities. Additionally, an edge computing gateway is integrated to facilitate the execution of algorithmic applications;
* An enhanced target detection model is meticulously designed, accompanied by the introduction of a sensor calibration mechanism.