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
Conclusion: This study provides a method to gradually recognize objects and can potentially assist
the operators in identifying prohibited objects.