# Occlusion Vehicle Target Recognition Method Based on Component Model
## 1 Introduction
[18] proposed the Recurrent Rolling Convolution (RRC) structure to improve the detection effect of the SSD algorithm on occlusion objects or small objects. However, this recognition method can only identify the occlusion generated between vehicles, and other objects are not good at identifying the occlusion of vehicles. The other is based on the structural features of the occluded target, using the prior knowledge and the structural information of the visible part of the target to design the detector to improve the occlusion detection performance. The authors of [19] proposed a vehicle recognition method based on multi-scale feature fusion. It combines high-level and low-level vehicle image features and can effectively deal with the complex and changeable natural environment in which the vehicle is located. In [20], a deformable part-based model (DPM) is proposed to detect vehicles. That is, a model is trained for each type of vehicle, and then these models are used in the traffic scene image for vehicle detection and extraction and feature alignment. Finally, a support vector machine is used for classification and recognition, but a large number of data sets are needed for training. The authors of [21] propose a vehicle detection framework. Firstly, an accurate vehicle proposal network (AVPN) based on a hyper-feature map is used to generate candidate vehicle regions. Then, a vehicle is used to attribute the learning network (VALN) to verify and classify candidate regions. In summary, researchers have conducted some research on occlusion object recognition to improve the reliability of occlusion detection. However, there is still inaccuracy in occlusion detection under the condition of large occlusion areas, complex backgrounds, and illumination change. Based on the advantages of deep learning in target recognition, considering the saliency and scale of vehicle targets, a vehicle target recognition network with component feature extraction is designed to improve its detection performance in occluded scenes. The contributions are summarized as follows:In order to solve the target detection in complex backgrounds, the improved U-Net module is proposed to segment the visual saliency region and filter out the background interference. 2. The Swin Transformer is adopted as the main unit of component feature extraction to add a component perception enhancement unit and an efficient attention unit to deal with the impact of target vehicle scale change, weak contrast, and regional interference on component regional feature extraction so that the module can efficiently extract component features for component classification. 3. By establishing a CMR (Component Mask Recognition) unit is used to gather the multi-scale features from the occluded vehicle components to learn and generate the feature mask to realize the classification to recognize the occluded vehicle targets. This paper includes the following outline. Section 2 introduces the structure of the VSRS-VCFM net. Section 3 introduces the experiments and results. The conclusions of this study are shown in Section 4.