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Quality control of apples by means of convolutional neural networks - Comparison of bruise detection by color images and near-infrared images

机译:通过卷积神经网络的苹果质量控制 - 彩色图像和近红外图像的瘀伤检测比较

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Different stages of logistic process can affect apples, which often results in illusive bruises - making it extremely hard for the personal who has to sort out these fruits quickly before packing. We aim to provide a solution for bruise detection. In this contribution, we use state of the art convolutional neural network architectures for the task. Simultaneous input is taken from color CMOS and near-infrared (with a bandwidth filter) cameras, illuminated with a special light source. We achieved an accuracy above 97% for bruise detection in both cases: Colored and near-infrared images, indicating that both options are equally suitable.
机译:物流过程的不同阶段可以影响苹果,这通常会导致虚幻的瘀伤 - 让个人在包装前快速整理这些果实的个人困难。我们的目标是提供瘀伤检测的解决方案。在这一贡献中,我们使用艺术卷积神经网络架构的状态来完成任务。同时输入是从COMOS和近红外线(带带宽过滤器)相机的彩色输入,用特殊光源照亮。我们在两种情况下实现了高于97%的准确性:彩色和近红外图像,表明这两个选项都同样适合。

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