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基于YOLO v5-TL的褐菇采摘视觉识别-测量-定位技术

Technology of Visual Identification-Measuring-Location for Brown Mushroom Picking Based on YOLO v5-TL

  • 摘要: 为实现褐菇高效、精准、快速的自动化采摘,针对工厂化褐菇的种植特点,提出一种基于YOLO v5迁移学习(YOLO v5-TL)结合褐菇三维边缘信息直径动态估测法的褐菇原位识别-测量-定位一体化方法。首先,基于YOLO v5-TL算法实现复杂菌丝背景下的褐菇快速识别;再针对锚框区域褐菇图像进行图像增强、去噪、自适应二值化、形态学处理、轮廓拟合进行褐菇边缘定位,并提取边缘点和褐菇中心点的像素坐标;最后基于褐菇三维边缘信息的直径动态估测法实现褐菇尺寸的精确测量和中心点定位。试验结果表明单帧图像平均处理时间为50 ms,光照强度低、中、高情况下采摘对象识别平均成功率为91.67%,其中高光强时识别率达100%,菇盖的尺寸测量平均精度为97.28%。研究表明,本文提出的YOLO v5-TL结合褐菇三维边缘信息直径动态估测法可实现工厂化种植环境下褐菇识别、测量、定位一体化,满足机器人褐菇自动化采摘需求。

     

    Abstract: To realize the efficient, accurate and rapid automatic picking of brown mushroom, the identification, size measurement and positioning of mushroom are the key to the robot selective picking operation. An integrated method for in situ identification, measurement and location of brown mushroom was proposed based on YOLO v5 transfer learning(YOLO v5-TL) and dynamic diameter estimation based on 3 D edge information. Firstly, YOLO v5-TL algorithm was used to realize rapid identification of brown mushroom under complex mycelia background. Then, the image enhancement algorithm, denoising, adaptive binarization algorithm, morphological processing and contour fitting algorithm were used to locate the edge of the mushroom image in the anchor frame area, meanwhile, the pixel coordinates of the edge point and the center point were extracted. Finally, the dynamic diameter estimation method based on 3 D edge information was used to accurately measure the size and locate the center point of the mushroom. The experimental results showed that the average processing time of single frame image was 50 ms. The average success rate of picking object recognition under low, medium and high light intensity was 91.67%, and the recognition rate reached 100% under high light intensity. The average measurement accuracy of mushroom cover was 97.28%. The results showed that the proposed YOLO v5-TL method combined with 3 D edge information diameter dynamic estimation method can realize the integration of identification, measurement and location of brown mushroom under factory planting, which met the demand of automatic picking of brown mushroom by robot.

     

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