A prohibited items identification approach based on semantic segmentation

Abstract:

Deep learning (DL) based semantic segmentation methods can extract object information including category, location and shape. In this paper, the identification of prohibited items is regarded as a task of semantic segmentation, and proposes a universal model with automatic identification of prohibited items. This model has two improvements based on the general semantic segmentation network. Firstly, the N-type encoding structure is applied to enlarge the receptive field of the network aiming at reducing the misclassification. Secondly, consider the lack of surface texture in X-ray security images. Inspired by feature reuse in Densenet, shallow semantic information is reused to improve the segmentation accuracy. With the use of this model, when using input images of size 512×512, we could achieve 0.783 mean intersection over union (mIoU) for a seven-class object recognition problem.