基于YOLO v5n与动态步长搜索的孢子显微成像自动对焦系统设计与试验

Design and Experiment of Auto-focus System for Spore Microscopic Imaging Based on YOLO v5n and Dynamic Step Search

摘要:

采用孢子智能捕捉仪一体化监测气传致病孢子已成为当前作物气传病害早期在线预警的重要手段。针对复杂工况下孢子捕捉仪采用固定焦距显微成像时易出现图像离焦模糊、孢子检测精度不高等问题，本研究以小麦条锈菌夏孢子为研究对象，设计了一种融合YOLO v5n目标检测与基于孢子形态尺寸的动态步长搜索策略的孢子显微成像自动对焦系统，旨在实现复杂背景下夏孢子显微成像所在焦平面的自适应追踪与计数。首先，基于树莓派微控制器和CMOS图像传感器搭建低成本的便携式显微图像采集装置，通过步进电机驱动镜筒垂直上下位移（1/8步细分模式，步长0.625μm），以完成多焦距的夏孢子显微图像序列采集；其次，创新性地将YOLO v5n模型与传统平方修正拉普拉斯算子（Squared modified laplacian, SML）梯度评价函数结合，提出改进的夏孢子对焦评价函数，解决背景杂质干扰导致的焦平面误判问题；最后，采用基于孢子形态尺寸的动态步长搜索策略（粗搜索10μm/步，精搜索2.5μm/步）优化显微成像的自动对焦效率。试验结果表明，改进夏孢子对焦评价函数的孢子计数精度为97.44%，比采用传统基于梯度的对焦评价函数的计数精度提高了56.54个百分点；显微成像对焦系统的自动对焦成功率为98%，平均自动对焦时间为116.49s。本研究提出的高对焦率、强鲁棒性自动对焦算法，配合低成本、快速响应的便携式显微图像采集装置，有效推动了智能孢子捕捉仪的自动化升级，为作物气传病害跨区域防控体系建设提供了关键技术支撑。

Abstract:

The integrated monitoring of airborne pathogenic spores using intelligent spore trapping devices has become a crucial approach for early online warning of crop airborne diseases. To address issues such as image defocus blur and low spore detection accuracy caused by fixedfocus microscopic imaging under complex working conditions, focusing on urediospores of wheat stripe rust, an automatic focusing system for spore microscopic imaging was designed. This system integrated YOLO v5n object detection with a spore morphology-adaptive dynamic step search strategy to achieve adaptive tracking and counting of urediospores in complex backgrounds. Firstly, a low-cost portable microscopic image acquisition device was constructed by using a Raspberry Pi microcontroller and CMOS image sensor. A stepper motor drived the lens barrel vertically (1/8 microstepping mode, step size 0.625μm) to capture multifocal urediniospore image sequences. Secondly, an improved spore focusing evaluation function was innovatively proposed by combining the YOLO v5n model with the traditional squared modified Laplacian (SML) gradient evaluation function, effectively solving the misjudgment of focal planes caused by background impurities. Finally, a spore morphology-adaptive dynamic step search strategy (coarse search: 10 micrometers per step;fine search: 2.5 micrometers per step) was implemented to optimize focusing efficiency. Experimental results demonstrated that the proposed evaluation function achieved 97.44% spore counting accuracy, representing a 56.54 percentagepoint improvement over that of traditional gradient-based methods. The automatic focusing success rate reached 98% with an average focusing time of 116.49s. The developed autofocusing algorithm (high accuracy/robustness) combined with the low-cost, fastresponse portable imaging device significantly advanced intelligent spore trap automation, offering key technological solutions for cross-regional management of airborne crop pathogens.