A lightweight and accurate recognition algorithm of pointer meter based on improved Deeplabv3+ for inspection robots

Abstract

Pointer meter is widely utilized in the fields of modern industry. Nowadays, intelligent inspection robots are gradually employed in place of labors for inspection task. Pointer meter recognition is one of the most important tasks of inspection robots. This article presents a lightweight pointer meter recognition algorithm, which is suitable for deployment on inspection robots. First, pointer meter is identified by pruned YOLOv5. Afterwards, the dial and the pointer are segmented through improved Deeplabv3+ in which the JPU and depthwise separable convolutions are utilized in lieu of dilated convolutions. After perspective transform and central-line extraction of pointer, the reading of the pointer meter can be determined using the angle method. Experimental results verify the FPS of pruned YOLOv5 improves 13.18% compared to original YOLOv5 and the FPS of improved Deeplabv3+ improves 45.74% compared to original Deeplabv3+. Additionally, the reading accuracy of the algorithm is 98.40% and average fiducial error is 0.32%, which indicate good accuracy. The relative standard deviation of reading is less than 1.4%, which indicates good stability of proposed algorithm. This study proposes a lightweight and accurate pointer meter recognition algorithm based on improved Deeplabv3+, the algorithm is ported to NVIDIA Jetson TX2 NX to verify its stability and accuracy.