Agarla Mirko, Napoletano Paolo, Schettini Raimondo
Dipartimento di Informatica, Sistemistica e Comunicazione, Università Milano-Bicocca, 20126 Milano, Italy.
Sensors (Basel). 2023 Sep 14;23(18):7893. doi: 10.3390/s23187893.
Defect segmentation of apples is an important task in the agriculture industry for quality control and food safety. In this paper, we propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs) based on a U-shaped architecture with skip-connections only within the noise reduction block. An ad-hoc data synthesis technique has been designed to increase the number of samples and at the same time to reduce neural network overfitting. We evaluate our model on a dataset of multi-spectral apple images with pixel-wise annotations for several types of defects. In this paper, we show that our proposal outperforms in terms of segmentation accuracy general-purpose deep learning architectures commonly used for segmentation tasks. From the application point of view, we improve the previous methods for apple defect segmentation. A measure of the computational cost shows that our proposal can be employed in real-time (about 100 frame-per-second on GPU) and in quasi-real-time (about 7/8 frame-per-second on CPU) visual-based apple inspection. To further improve the applicability of the method, we investigate the potential of using only RGB images instead of multi-spectral images as input images. The results prove that the accuracy in this case is almost comparable with the multi-spectral case.
苹果缺陷分割是农业产业中质量控制和食品安全的一项重要任务。在本文中，我们提出了一种深度学习方法，用于基于仅在降噪模块内具有跳跃连接的U型架构的卷积神经网络（CNN）对苹果缺陷进行自动分割。我们设计了一种特殊的数据合成技术来增加样本数量，同时减少神经网络的过拟合。我们在一个多光谱苹果图像数据集上评估我们的模型，该数据集具有针对几种缺陷类型的逐像素标注。在本文中，我们表明我们的方法在分割精度方面优于通常用于分割任务的通用深度学习架构。从应用角度来看，我们改进了先前的苹果缺陷分割方法。对计算成本的衡量表明，我们的方法可以实时应用（在GPU上约每秒100帧）以及准实时应用（在CPU上约每秒7/8帧）于基于视觉的苹果检测。为了进一步提高该方法的适用性，我们研究了仅使用RGB图像而非多光谱图像作为输入图像的潜力。结果证明，在这种情况下的精度几乎与多光谱情况相当。
