# Occlusion Vehicle Target Recognition Method Based on Component Model
## 1 Introduction  
At present, the number of urban vehicles is increasing, and traffic congestion and traffic accidents are frequent, which brings difficulties to road monitoring and traffic supervision and has become an important factor hindering current social development [1]. In order to solve the traffic problem, all countries are actively studying intelligent transportation systems, and one of the important research contents of intelligent transportation systems is vehicle recognition [2, 3]. Although computer vision recognition technology has made great progress, in the complex urban road environment, due to illumination, imaging deformation, vehicle target occlusion [4], and other factors, the camera often cannot capture the vehicle target image with high contrast and constant geometry, especially the complete vehicle target image, resulting in the loss of vehicle target information and the introduction of noise information. This results in a large intra-class difference in vehicle features, resulting in a great reduction in the accuracy of the recognition algorithm, which brings difficulties to the monitoring of traffic vehicles. Therefore, how to effectively recognize and analyze partially occluded vehicle targets is the focus of current research [5]. According to the relationship between the occlusion and the occluded object, occlusion can be divided into inter-target occlusion, perspective occlusion, and non-target occlusion [6]. Mutual occlusion between targets, such as mutual occlusion between vehicles, canbe solved by optimizing the detection method when the training samples are sufficient. The other is the occlusion of non-target objects. Due to the variety of occluded objects, there is often a lack of effective labeling information, which makes it difficult for the target detection module to process directly. At present, for the problem of inter-target occlusion and perspective occlusion, the overall analysis of the feature map is proposed to achieve the overall detection of the target, including the DeepParts [7] module and OR-CNN (occlusion-aware R-CNN) network [8]. These detection algorithms are based on partial semantics. Aiming at the occlusion problem caused by non-target objects [9], [PERSON] and [PERSON] [10] proposed a regularization technique to enhance the data, called \"Cutout\", to prevent the convolutional neural network from overfitting and causing poor occlusion adaptability. Cutout is similar to Dropout [11], which discards some of the features to enhance the robustness of the neural network. Cutout and Dropout delete features by covering black pixels or random noise to remove pixel information on the training image, but it will lead to information loss and low efficiency in training. [PERSON] et al. [12] proposed a regularization method called \"CutMix\". The strategy is to cut and paste occlusion blocks between the training images for image enhancement and blend the target area into the merged image proportionally, thus avoiding the negative impact of the network on the pixels that lack information in the training. In recent years, the research on vehicle recognition can be divided into two categories [13]. The first is to restore the foreground occlusion part and realize the recognition method. Related methods can be roughly divided into autoencoder-based image restoration [14], a vehicle target detection algorithm based on the generative adversarial network [15], and a vehicle image detection method based on context-aware semantics [16]. [PERSON] et al. [17] proposed a multi-label learning approach to co-learn component detectors. For each component detector, the overall image of the component is used as training data. This joint learning method effectively improves the detection accuracy of the component detectors, but it cannot get rid of the large number of computational resources required to train the component detectors separately. [PERSON] et al.