One of the persistent problems involved in the technology of automated machine vision apple defect sorting lies in the discrimination between true defects and the stem-end/calyx of fruit. To solve this problem, a novel method was developed which incorporates a near-infrared (NIR) camera and a mid-infrared (MIR) camera for simultaneous imaging of the fruit being inspected. The NIR camera is sensitive to both the stem-end/calyx and true defects; whereas the MIR camera is only sensitive to the stem-end and calyx. True defects can be quickly and reliably extracted by logical comparison between the processed NIR and MIR images. A 98.86 recognition rate for stem-ends and a 99.34 recognition rate for calyxes were achieved using a dual-camera NIR/MIR machine vision defect sorting system.
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机译：自动机器视觉苹果缺陷分选技术中涉及的一个长期问题在于对真正的缺陷和果实的茎端/花萼的区分。为了解决这个问题，开发了一种新的方法，该方法结合了近红外（NIR）相机和中红外（MIR）相机，用于同时对被检测的水果进行成像。近红外相机对茎端/花萼和真缺陷都很敏感;而 MIR 相机仅对茎端和花萼敏感。通过对处理后的近红外和MIR图像进行逻辑比较，可以快速可靠地提取真正的缺陷。采用双相机NIR/MIR机器视觉缺陷分选系统，茎端识别率达98.86%，花萼识别率达99.34%。
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