Automatic detonator code recognition via deep neural network.

• An expert and intelligent system is developed for detonator code recognition. • Multi-scale concatenation of convolutions accelerates speed in inference. • Progressive self-attention boosts performance in recognition. • Release a publicly available annotated detonator image dataset. • An end-to-end recognition accuracy of 99.18% is achieved. Detonators are hazardous and must be strictly controlled to strengthen public security since they contain explosive. To deal with the stressful management of detonators by manually recording detonator codes, an expert and intelligent system for detonator code recognition is an alternative management scheme for automatically recording detonator codes. How to achieve accurate and reliable recognition performance is also a challenging problem in such an intelligent system. In this study, we develop an intelligent vision-based system based on deep learning for identifying detonator codes. Specifically, we design a detonator image acquisition subsystem to construct the detonator code image dataset, and an intelligent image processing subsystem named as automatic detonator code recognition network (ADCR-Net). The ADCR-Net is a pipeline cascading a deep localization network and a deep recognition network. The multi-scale concatenation block and the features integration module are proposed for the localization network of the ADCR-Net to improve the features expressions. The multi-level progressive self-attention block is put forward for the recognition network of the ADCR-Net to help with focusing on multi-level activation maps. Also, a multi-label loss function for the recognition network is designed to balance the significance of the classifiers of the intelligent system during training. Experiments on our released dataset demonstrate the effectiveness and efficiency of the proposed expert and intelligent system, which achieves 99.18% accuracy in end-to-end recognition. Our work provides an effective resolution to the expert and intelligent system for automatic detonator code recognition. Also, we provide an alternative framework to deal with the tradeoff between inference time and recognition accuracy in deep learning which is becoming a popular method in many expert and intelligent systems. Furthermore, the released dataset is available for distribution to the related researchers to develop their expert systems in the field of industrial optical character recognition. [ABSTRACT FROM AUTHOR]