Abstract
Background:: Owing to the ability of a deep learning algorithm to identify objects and the related detection technology of security inspection equipment, in this paper, we propose a progressive object recognition method that considers local information of objects. Methods:: First, we construct an X-Base model by cascading multiple convolutions and pooling layers to obtain the feature mapping image. Moreover, we provide a “segmented convolution, unified recognition” strategy to detect the size of the objects. Results:: Experimental results show that this method can effectively identify the specifications of bags passing through the security inspection equipment. Compared with the traditional VGG and progressive VGG recognition methods, the proposed method achieves advantages in terms of efficiency and concurrency. Conclusion:: This study provides a method to gradually recognize objects and can potentially assist the operators in identifying prohibited objects.
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