# A Multidimensional Data Collection and Edge Computing Analysis Method
## 3 Overall System Architecture and Improved Target Detection Model
### Object Detection and Sensing Data Correction
#### 3.2.1 U-Net-Based Image Super-Resolution Pre-Processing Model Construction  
In image monitoring of public space, we encounter some acquisition time points with moderate clarity, such as night, cloudy day, etc. The blurred images acquired at this time have an impact on the consecutive target detection and the calculation of spatial utilization. Accordingly, in this system, we first perform sharpness processing on the acquired image dataset. The structure diagram of the image super-resolution processing using U-Net is shown in Figure 3a. Figure 2: The overall structure of the system and the construction of public space utilization. In the training phase of the model, the method utilizes single angle and multiple angle images as training pairs, and feeds single-angle low-resolution images into the U Net convolutional neural network. Taking multi angle high-resolution images as reference images, an optimized end-to-end mapping is obtained through iteration of network weights. In recent years, U Net has been utilized predominantly in the field of medical imaging, as it has super segmentation ability. We apply the U Net network to the image reconstruction network, which consists of an encoding path and a decoding path. The encoding path extracts abstract features by shrinking the input image layer-by-layer, while the decoding path expands the feature map layer-by-layer through the deconvolution operation on the abstract features to retrieve the final reconstructed image. The encoding path consists of five convolutional blocks (each convolutional block consists of two \(3\times 3\) convolutional layers), combined with a maximum pooling layer with a stride of 2, to accomplish the expansion of the number of feature channels from 1 to 1024 and the feature size is reduced from 608 \(\times\) 608 to 38 \(\times\) 38. The decoding path contains a total of five convolution blocks. The number of feature channels is diminished to 1 through the deconvolution layer between each convolution block, and the size of the feature map is expanded from 38 \(\times\) 38 to 608 \(\times\) 608. Among them, the copying and splicing layer establishes mirror connections between feature maps with the same resolution in the encoding and decoding paths to compensate for the lack of contextual information caused by the deep network. In the encoder backend, a \(1\times 1\) convolutional layer is used to enhance the network's ability to convey complex features. In addition, the network adopts batch normalization (BN) to accelerate the convergence speed of the U-Net model and improve the gradient dispersion problem of the model. The network chooses the Max out cell as the activation function and its principle is shown in Figure 2(b)., which can effectively construct the nonlinear mapping relationship and enable the U-Net network to fit any convex function. At the same time, the network can effectively avoid dead cells. The Max out unit can be defined as:  
\[Z_{j}=X^{T}W_{j}+b_{j} \tag{1}\]  
\[h(x)=\max_{j\in[1\mathcal{K}]}Z_{j} \tag{2}\]  
Among them, \(Z_{j}\) represents the activation value of the \(j(jek)\) \"hidden layer\" node. \(W_{j}\) and \(b_{j}\) represent the weight and bias of the node, respectively. \(h(x)\) represents the final output of the Max out activation function. The choice of loss function combines the mean square error and structural similarity information, which can comprehensively learn detailed features and structural features.The loss function is defined as:  
Figure 3: Super-resolution image network structure: (**a**) U-Net composite network structure; (**b**) Max-out activation function.