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基于图像融合的不同成熟阶段苹果果实识别OACSTPCD

Apple fruit recognition at different maturity stages based on image fusion

中文摘要英文摘要

[目的]针对复杂农业环境中不同成熟阶段苹果目标识别困难的问题,研究一种基于图像融合的苹果识别算法.[方法]采用保边性能较好的均值漂移滤波对图像进行预处理,滤除少量背景噪声.分别从RGB颜色空间和YIQ颜色空间提取R-G分量和I分量特征图像,采用像素级图像融合算法融合 2 幅特征图像信息,突出显示果实目标区域.利用Otsu自适应阈值算法获得最佳阈值,将目标苹果从背景中分割出来.为识别苹果目标,提出一种基于改进梯度场的Hough变换圆检测算法,通过引入形态学重建算法清理背景中残留的小面积区域,提高检测效率;同时以分割的苹果二值图像为判断标准构造剔除虚假圆算法,避免检测出现虚假目标.[结果]对采集到的 50 幅未完全成熟的苹果图像和 50 幅完全成熟的苹果图像进行识别,并与最小外接圆法进行对比,试验结果表明,本文算法平均识别时间为 0.367 s,对完全裸露果实、被遮挡面积≤1/2 果实和被遮挡面积>1/2 果实的识别正确率分别为 100%、92.46%,和 81.87%,整体识别准确率比最小外接圆算法提高了 11.43 个百分点.本文算法圆心相对误差均值和半径相对误差均值分别为 0.216 和 0.048%,最小外接圆算法圆心相对误差均值和半径相对误差均值分别为 0.508 和 0.370%.[结论]本文提出的方法能够快速识别苹果目标,具有较高精度和效率的果实定位,可以服务于苹果采摘机器人进行果实采摘.

[Objective]To address the challenge of apple target recognition in different mature stages in a complex agricultural environment,an apple recognition algorithm based on image fusion was studied.[Method]A mean-shift filter with good edge-preserving performance was used to preprocess the image and filter out small parts of background noise.The R-G component and the I component feature images were extracted from RGB color space and YIQ color space,respectively.The pixel-level image fusion algorithm was used to fuse the information of two feature images to highlight the fruit target area.The Otsu adaptive threshold algorithm was used to obtain the best threshold to segment the target apple from the background.In order to identify the apple target,an improved Hough transform circle detection algorithm based on gradient fields was proposed.The morphological reconstruction algorithm was introduced to clean up the small area remaining in the background so as to improve the detection efficiency.At the same time,the algorithm to eliminate false circles was constructed using the segmented binary image of apple as the judgment standard,so as to avoid false targets.[Result]Fifty images of immature apples and fifty images of fully matured apples were recognized and compared with the minimum circumscribed circle method.The experiment results showed that the average recognition time of the algorithm in this paper was 0.367 s,and the recognition accuracy for completely exposed fruits was 100%,92.46%for fruits with occluded areas≤1/2,81.87%for fruits with occluded areas>1/2.The overall recognition accuracy was increased by 11.43 percentage points compared with that of the minimum circumscribed circle algorithm.The mean center relative error and the mean radius relative error of the algorithm in this paper were 0.216 and 0.048%respectively,and the mean center relative error and the mean radius relative error of the minimum circumscribed circle algorithm were 0.508 and 0.370%respectively.[Conclusion]The method proposed in this paper can quickly identify the apple target,locate the fruit with high accuracy and efficiency,and can be used for fruit picking by the apple picking robot.

刘茗洋;崔凯;宫金良;张彦斐

山东理工大学农业工程与食品科学学院,山东淄博 255000南京蜻蜓智慧农业研究院有限公司,江苏南京 210019山东理工大学机械工程学院,山东淄博 255000

农业工程

苹果;图像融合;Hough变换;成熟阶段

Apple;Image fusion;Hough transform;Ripening stage

《华南农业大学学报》 2024 (002)

293-303 / 11

山东省重点研发计划(2020CXGC010804);山东省自然科学基金(ZR202102210303);淄博市重点研发计划(2019ZBXC200)

10.7671/j.issn.1001-411X.202212020

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