<p>Measurement devices such as digital meters are used in many engineering applications to provide valuable data for analysis, control, and optimization. However, the current manual techniques and automated reading algorithms have limitations in achieving accurate and robust reading recognition under challenging situations such as light reflections on the target device’s screen that occlude the reading information. In this work, a novel framework is proposed to enhance digital meters inspection and reading based on specular reflection-aware human–machine interaction. The proposed method involves a remotely controlled unmanned aerial vehicle (UAV), convolutional neural network-based model for automated digital meter detection, light reflections, and specular-highlight-aware human–machine interaction strategy to capture better quality images for inspection and data extraction. The experimental results based on a custom dataset of 560 augmented digital meter images (640 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times \)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> 640) and a separate test set collected from various publicly available resources on the internet to consider diverse visual conditions reveal that the proposed approach enables detecting the digital meter with an average precision of 0.943, very high recall scores, and with the a short detection time, which is only 0.564s compared with other related works.</p>