论文标题
用于基于图像的玉米内核检测和计数的卷积神经网络
Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting
论文作者
论文摘要
精确的玉米谷物产量估计，使农民能够实时准确收获和谷物营销决策，从而最大程度地减少了盈利能力的损失。发达的玉米耳朵最多可以具有800个内核，但是手动计算玉米耳朵上的内核是劳动密集型，耗时且易于人为错误。从算法的角度来看，由于大量的核以不同的角度和核之间的距离非常小，因此从单个玉米耳朵图像中检测核的检测是具有挑战性的。在本文中，我们建议基于滑动窗口方法的内核检测和计数方法。所提出的方法检测并计数在不受控制的照明条件下拍摄的单玉米耳朵图像中的所有玉米粒。滑动窗口方法使用卷积神经网络（CNN）进行内核检测。然后，应用非最大抑制（NMS）以删除重叠的检测。最后，被归类为内核的窗口将传递给另一个CNN回归模型，以查找内核图像贴片中心的（x，y）坐标。我们的实验表明，所提出的方法可以成功检测出低检测误差的玉米粒，并且还能够在以不同角度定位的一批玉米耳朵上检测核。
Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detect and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the (x,y) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.
