The object detection method of multi-view Single Shot multibox Detector(SSD) based on deep learning was proposed. Firstly, the model and the working principle of classical SSD were expounded. According to the concept of convolution receptive field and the mapping relationship between the feature map and the original image, the sizes of covolution receptive field in different levels and the scales of the default boxes mapped to the original image were analyzed to find the reason why the classical SSD was not good at small object detection. Based on this, the multi-view SSD model was put forward, and the model architecture and its working principle were deeply expounded. Then, through the test in a dataset of 106 images for small object detection, the detection performance of multi-view SSD and classical SSD were evaluated and compared in object retrieval ability and object detection precision. Experimental results show that with the confidence threshold of 0.4, the multi-view SSD is 0.729 in Average F-measure(AF) and 0.644 in mean Average Precision(mAP), and has respectively raised 0.169 and 0.131 compared to the classical SSD in the two evaluation indexes, thus verifying the effectiveness of the proposed method.