Occluded Meter Reading with Pointer Mask Generation Based on Generative Adversarial Network

Machine vision-based meter reading technologies have been developed to monitor the status of devices in industrial sites. However, the integrity of the pointer can be destroyed by the occlusion on the meter, such as dirt or rain. In this case, the pointer detection becomes a challenging task in the meter reading. To overcome this challenge, in this paper, the pointer generative adversarial network (Pointer-GAN) is proposed for pointer mask generation. Specifically, an occlusion simulation method is developed in the data preprocessing to provide sufficient images of occluded meters in the training phase, and the dilated convolution is adopted in the residual block to strengthen the correlation among pointer features in the long-range. The next, the multi-scale attention mechanism is designed for preventing the pointer feature in the low-level from being affected by the noise. Finally, the dense dilated convolution block is utilized to integrate the pointer feature in the low- and high-level for the pointer mask generation. The experiments demonstrate that the Pointer-GAN can generate the pointer mask with higher accuracy for the meters under occlusion compared to the other methods, thereby improving success rates of reading meters in different occlusion scenarios.