  Analog meters equipped with one or multiple pointers are wildly utilized to
monitor vital devices' status in industrial sites for safety concerns. Reading
these legacy meters {\bi autonomously} remains an open problem since estimating
pointer origin and direction under imaging damping factors imposed in the wild
could be challenging. Nevertheless, high accuracy, flexibility, and real-time
performance are demanded. In this work, we propose the Vector Detection Network
(VDN) to detect analog meters' pointers given their images, eliminating the
barriers for autonomously reading such meters using intelligent agents like
robots. We tackled the pointer as a two-dimensional vector, whose initial point
coincides with the tip, and the direction is along tail-to-tip. The network
estimates a confidence map, wherein the peak pixels are treated as vectors'
initial points, along with a two-layer scalar map, whose pixel values at each
peak form the scalar components in the directions of the coordinate axes. We
established the Pointer-10K dataset composing of real-world analog meter images
to evaluate our approach due to no similar dataset is available for now.
Experiments on the dataset demonstrated that our methods generalize well to
various meters, robust to harsh imaging factors, and run in real-time.
