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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Related Experiment Video

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Lensless Fluorescent Microscopy on a Chip

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A Two-Level Illumination Correction Network for Digital Meter Reading Recognition in Non-Uniform Low-Light

Haoning Fu¹, Zhiwei Xie², Wenzhu Jiang²

¹College of Computer Science and Cybersecurity, Chengdu University of Technology, Chengdu 610059, China.

Journal of Imaging

|April 27, 2026

Summary

This study introduces a coarse-to-fine cascaded perception framework (CFCP) for accurate digital instrument reading recognition. The CFCP effectively handles low-light conditions and noise, improving automation in industrial inspections.

Keywords:

YOLOv10 autonomous recognition deep learning digital meter reading non-uniform low-light conditions

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Area of Science:

Computer Vision
Industrial Automation
Image Processing

Background:

Automatic reading recognition of digital instruments is vital for industrial automation and inspection.
Non-standard industrial environments with low-light and reflective surfaces degrade recognition performance.
Conventional methods struggle with illumination recovery and noise suppression, leading to overexposure and amplified noise.

Purpose of the Study:

To address challenges in illumination recovery and noise suppression for digital instrument panel image quality restoration.
To propose a novel framework for accurate meter reading recognition under challenging industrial conditions.

Main Methods:

A coarse-to-fine cascaded perception framework (CFCP) is proposed.
Utilizes a lightweight YOLOv10 detector for initial region localization.
Incorporates an Adaptive Illumination Correction Module (AICM) for pixel-level illumination correction.
Employs an Illumination-invariant Feature Perception Module (IFPM) for feature-level noise filtering.
A lightweight sequence recognition network refines the detection for final readings.

Main Results:

The proposed CFCP achieves 93.2% recognition accuracy on a self-built industrial dataset.
Demonstrates a low latency of 17.1 ms.
Features a compact model size with only 7.9 M parameters.

Conclusions:

The CFCP framework effectively restores image quality and suppresses noise in non-standard industrial environments.
Achieves high accuracy and efficiency for automatic digital instrument reading recognition.
Offers a promising solution for enhancing metering automation and intelligent inspection.