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首页> 外文期刊>Computers and Electronics in Agriculture >Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities
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Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities

机译：使用RGB-D摄像机的富士苹果检测多模态深度学习及其辐射射线能力

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摘要

Fruit detection and localization will be essential for future agronomic management of fruit crops, with applications in yield prediction, yield mapping and automated harvesting. RGB-D cameras are promising sensors for fruit detection given that they provide geometrical information with color data. Some of these sensors work on the principle of time-of-flight (ToF) and, besides color and depth, provide the backscatter signal intensity. However, this radiometric capability has not been exploited for fruit detection applications. This work presents the KFuji RGB-DS database, composed of 967 multi-modal images containing a total of 12,839 Fuji apples. Compilation of the database allowed a study of the usefulness of fusing RGB-D and radiometric information obtained with Kinect v2 for fruit detection. To do so, the signal intensity was range corrected to overcome signal attenuation, obtaining an image that was proportional to the reflectance of the scene. A registration between RGB, depth and intensity images was then carried out. The Faster R-CNN model was adapted for use with five channel input images: color (RGB), depth (D) and range-corrected intensity signal (S). Results show an improvement of 4.46% in F1-score when adding depth and range-corrected intensity channels, obtaining an F1-score of 0.898 and an AP of 94.8% when all channels are used. From our experimental results, it can be concluded that the radiometric capabilities of ToF sensors give valuable information for fruit detection.
机译：水果检测和定位对于未来的水果作物的农艺管理至关重要，具有产量预测，产量测绘和自动收获的应用。 RGB-D相机是有前途的传感器，用于水果检测，因为它们提供了具有颜色数据的几何信息。其中一些传感器采用飞行时间（TOF）的原理，除了颜色和深度之外，提供反向散射信号强度。然而，这种辐射能力尚未利用果实检测应用。这项工作介绍了Kfuji RGB-DS数据库，由967个多模态图像组成，其中总共12,839个富士苹果。数据库的编译允许研究用Kinect V2获得的熔化RGB-D和辐射信息进行果实检测。为此，校正信号强度为克服信号衰减，获得与场景的反射率成比例的图像。然后进行RGB，深度和强度图像之间的登记。更快的R-CNN模型适用于五个通道输入图像：颜色（RGB），深度（D）和范围校正的强度信号。结果在添加深度和范围校正的强度通道时，在F1分数中提高了4.46％，获得0.898的F1分数，当使用所有通道时为0.898的F1分数，AP为94.8％。从我们的实验结果来看，可以得出结论，TOF传感器的辐射能力为水果检测提供了有价值的信息。

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