Read Pointer Meters in complex environments based on a Human-like Alignment and Recognition Algorithm

TL;DR

This paper introduces a human-like alignment and recognition algorithm for automatic reading of analog meters, improving robustness and speed in complex environments with low-quality images.

Contribution

The paper presents a novel end-to-end learnable framework with a Spatial Transformed Module and Value Acquisition Module, mimicking human behavior for meter reading.

Findings

High accuracy in meter reading across diverse conditions

Robust performance on low-quality images

Enhanced speed compared to existing methods

Abstract

Recently, developing an automatic reading system for analog measuring instruments has gained increased attention, as it enables the collection of numerous state of equipment. Nonetheless, two major obstacles still obstruct its deployment to real-world applications. The first issue is that they rarely take the entire pipeline's speed into account. The second is that they are incapable of dealing with some low-quality images (i.e., meter breakage, blur, and uneven scale). In this paper, we propose a human-like alignment and recognition algorithm to overcome these problems. More specifically, a Spatial Transformed Module(STM) is proposed to obtain the front view of images in a self-autonomous way based on an improved Spatial Transformer Networks(STN). Meanwhile, a Value Acquisition Module(VAM) is proposed to infer accurate meter values by an end-to-end trained framework. In contrast to previous research, our model aligns and recognizes meters totally implemented by learnable processing, which mimics human's behaviours and thus achieves higher performances. Extensive results verify the good robustness of the proposed model in terms of the accuracy and efficiency.