# Occlusion Vehicle Target Recognition Method Based on Component Model
## 2 Occlusion Vehicle Target Segmentation and Recognition Scheme Design
### Design of Visual Saliency Region Segmentation (VSRS) Module Based on Complex Backgrounds  
In order to realize the fast segmentation of the salient vehicle area in the image, the attention mechanism mimics human vision to find prominent areas that may be targets and then confirm them [22]. The U-Net [23] can be applied to salient region detection, but when the vehicle object is in a cluttered background, the detection accuracy and speed will be affected. Aiming at this problem, a saliency target detection module is designed. The nested U-shaped structure can effectively extract the multi-scale features of each stage and aggregate the multi-stage multi-level features so as to improve the accuracy of vehicle target segmentation in complex backgrounds. The module structure is shown in Figure 2a. Each blue square is a Residual U-block (RSU). The downward line represents the down-sampling, the upward line represents the up-sampling, and the right line represents the  
Figure 1: Vehicle recognition method based on component module.  
jump connection, that is, two features of the same height and width are connected in series on the channel dimension.  
The structure of the RSU module is shown in Figure 2b. The structure includes up-sampling and down-sampling. Each layer of the net contains a convolution layer, a batch normalization layer, and an activation layer. \(H\), \(W\), and \(C\) represent the height, width, and number of channels of input and output; \(M\) is the measure of the number of input and output channels of each layer; the convolution kernel size of the convolution layer is \(3\times 3\); and \(D\) is the expansion rate of the dilated convolution.  
The RSU is composed of a mixture of different receptive fields, so the VSRS module can obtain multi-scale target information and filter out local complex backgrounds. At the same time, the pooling operation of the RSU can improve the depth of the entire module architecture, facilitate the acquisition of global information, and filter out the global complex background. Since the VSRS module is based on the RSU and does not use any pre-trained backbone network for image classification, the constructed saliency target detection net module has flexible performance and strong adaptability, which is conducive to the realization of complex background filtering.  
In the training process, the pre-training and supervised learning methods are used to optimize the net parameters. In order to consider both the module classification performance and the structure of the segmentation target, the loss function is composed of two parts: binary cross entropy loss and structural similarity loss. The binary cross entropy \(loss_{BCE}\) is  
\[loss_{BCE}=-\sum_{(x,y)}^{(H,W)}\left[L\cdot p(x,y)+(1-L)\log(1-p(x,y))\right] \tag{1}\]  
In Equation (1), \(p(x,y)\) and \(L\) represent the module's predicted value and the true value of the image coordinate point \((x,y)\) with height and width \(W\).  
The second part of the loss function uses regional data to calculate the structural similarity loss as follows:  
\[loss_{SSIM}=1-\frac{(2\mu_{I}\mu_{S}+C_{1})(2\sigma_{IS}+C_{2})}{\left(\mu_{I} ^{2}+\mu_{S}^{2}+C_{1}\right)\left(\sigma_{I}^{2}+\sigma_{S}^{2}+C_{2}\right)} \tag{2}\]  
In Equation (2), \(I\) and \(S\) represent the two images of the input and the output in the VSRS module to be compared, \(\mu_{I}\) and \(\mu_{S}\) are their average values, \(\sigma_{I}^{2}\) and \(\sigma_{S}^{2}\) are their variances, \(\sigma_{IS}\) is the covariance, and \(C_{1}\) and \(C_{2}\) are constants to increase stability.  
Figure 2: Visual saliency region segmentation module. (**a**) Visual saliency region segmentation structure, (**b**) residual U-block.