An ASMO method for CNN-based Occluded Object Detection

Recently, object detection technology using deep learning has been rapidly developed. In this paper, we propose a method of generating trainset by automatically merging objects to recognize occluded objects using deep learning. Since the objects are merged automatically by using various distances and angles among objects, generated trainset can cover a lot of different cases while saving time and labor compared to manual labeling. The proposed system achieves a correct detection rate of 88.43% as a mean average precision (mAP) for 15 multiple objects.