论文标题
基于知识蒸馏压缩的Yolov5对柑橘类水果的产量评估
Yield Evaluation of Citrus Fruits based on the YoloV5 compressed by Knowledge Distillation
论文作者
论文摘要
在种植果树的领域，收获前的果实产量估计对于水果储存和价格评估很重要。但是，考虑到成本，无法通过直接选择未成熟的水果来评估每棵树的产量。因此，问题是一项非常困难的任务。在本文中，提出了一种基于计算机视觉的水果计数和产量评估方法，以柑橘类果树为例。首先，从不同角度获取单个果树的图像，并使用深卷积神经网络模型Yolov5检测到水果的数量，并使用知识蒸馏方法压缩该模型。然后，使用线性回归方法来建模与产量相关的特征并评估产量。实验表明，所提出的方法可以准确计算水果并近似产量。
In the field of planting fruit trees, pre-harvest estimation of fruit yield is important for fruit storage and price evaluation. However, considering the cost, the yield of each tree cannot be assessed by directly picking the immature fruit. Therefore, the problem is a very difficult task. In this paper, a fruit counting and yield assessment method based on computer vision is proposed for citrus fruit trees as an example. Firstly, images of single fruit trees from different angles are acquired and the number of fruits is detected using a deep Convolutional Neural Network model YOLOv5, and the model is compressed using a knowledge distillation method. Then, a linear regression method is used to model yield-related features and evaluate yield. Experiments show that the proposed method can accurately count fruits and approximate the yield.
