Polarization imaging technology, compared to conventional imaging technology, can enhance contrast and visibility, especially in eliminating reflections or scatter, while also revealing specific properties and details of object surfaces. Multi-view imaging allows capturing scenes from different angles, offering richer scene information and depth perception, thus enhancing the capability to capture image details. However, traditional multi-view object detection focuses solely on ordinary images, neglecting the polarization information contained within the scene. To address this issue, this study employs a self-developed multi-view polarization camera array system to identify and recover occluded targets within a scene. Initially, the system acquires multi-view polarization information of the scene, including data on pure occlusions, ground truth, and occluded scenes across different distances, obstructions, and objects. Subsequently, the acquired polarized images are preprocessed, and polarization images at 0°, 45°, 90°, and 135°are fed into the Polar-ReOccNet network for supervised training, with occluded scene data corresponding to ground truth. Ultimately, by inputting a set of multi-view polarized images, one can obtain the polarized images of the target after removing occlusions, and calculate its degree of polarization, angle of polarization, and a fused image of both degree and angle of polarization. This method achieves the reconstruction of polarization information for occluded targets, effectively retrieving the polarization information inherent in properties such as target texture and materials, thereby facilitating object detection.