Reading Various Types of Pointer Meters Under Extreme Motion Blur

Automatically reading pointer meters using deep learning has yielded promising results with high precision. However, existing methods ignore the interference with the camera brought by moving devices (e.g., patrol robots and drones), thus the persistent motion blur caused by the camera shake is not properly addressed. It is noteworthy that reading the pointer meter relies heavily on semantic segmentation of the scale and pointer within the meter. However, this can be challenging in extreme motion blur and diverse substation scenes. Moreover, reading various types of pointer meters and out-of-range pointer check remain tough issues. Thus, in this study, a full pipeline is proposed to solve the problems mentioned above. First, Filter-Deblur-U-net (FD-U-net) is proposed to ensure accurate segmentation under motion blur. To be specific, FD-U-net is a one-stage network consisting of a deblurring module and a segmentation module. The segmentation loss supervises the optimization of the deblurring module. And the proposed high-frequency residual attention (HFRA) in FD-U-net meticulously refines the details of motion-blurred image at the texture accumulated stage. Furthermore, the judgment-reading-algorithm (JRA) is developed to complete readings of 35 types of meters. To ensure practical application, we propose the data augmentation strategy called motion-blur-MixUp (MB-MixUp) to maintain precise meter localization under motion blur. Additionally, we propose a method called dark channel prior dehaze Laplace (DCPD-Laplace) to determine whether the meter patch is motion-blurred. Experimental results have demonstrated the whole pipeline achieves state-of-the-art performance with average relative error and average reference error of only 1.54% and 0.48%, respectively.