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首页> 外文期刊>Computers and Electronics in Agriculture >Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure
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Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure

机译：具有高杂草压力的田间地下油菜图像地面油菜图像语义分割的微调卷积神经网络

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摘要

Image processing technology has gained considerable attention in agricultural proximal sensing applications, including plant disease detection, vegetation fraction estimation, monitoring of the crop growth status, and image-based site-specific management. Image segmentation is the first and crucial step to process complex infield images. However, image segmentation by either hand engineered-based or deep learning-based methods that train the entire system from scratch is a daunting task and needs several hundreds of labeled images that may be difficult to obtain in practice. The recent development of transfer learning has shown the potential of transferring the learned feature detectors of a pre-trained convolutional neural network to a new image dataset. This study was thus aimed to evaluate three transfer learning methods using a VGG16-based encoder net for semantic segmentation of oilseed rapes images in a field with high-density weeds. Three different transfer learning approaches using a VGG16-based encoder model were proposed, and their performances were compared to a VGG19-based encoder net. Relying on the intensive use of data augmentation and transfer learning, we showed that such networks could be trained end-to-end using a few annotated training images. The highest accuracy of 96% was obtained by the VGG16-based encoder net in which the fine-tuned model was only used for feature extraction and the segmentation was performed using shallow machine learning classifiers (MLCs). Transfer learning demonstrated to be efficient and presented a robust performance in segmenting plants amongst high-density weeds. The implementation of MLCs is reasonable for real-time applications with the segmentation time less than 0.05 s/image.
机译：图像处理技术在农业近端传感应用中取得了相当大的关注，包括植物疾病检测，植被分数估计，监测作物生长状态，以及基于形象的现场特定管理。图像分割是处理复杂infield图像的第一和关键步骤。然而，通过从头划痕训练整个系统的基于手工工程的基于或深度学习的方法的图像分割是令人生畏的任务，并且需要在实践中可能难以获得数百个标记的图像。最近的转移学习的发展已经表明，将预先训练的卷积神经网络的学习特征探测器转移到新的图像数据集的可能性。因此，本研究旨在使用基于VGG16的编码器网络评估三个转移学习方法，用于具有高密度杂草的领域的油籽强奸图像的语义细分。提出了使用基于VGG16的编码器模型的三种不同的传输学习方法，并将其性能与基于VGG19的编码器网进行了比较。依靠密集使用数据增强和转移学习，我们展示了这些网络可以使用一些注释的训练图像进行训练结束到底。通过基于VGG16的编码器网获得的最高精度为96％，其中微调模型仅用于特征提取，并且使用浅机器学习分类器（MLC）进行分割。转移学习证明是高密度杂草之间的分段工厂的稳健性能。 MLC的实现是合理的，用于实时应用，分割时间小于0.05秒/图像。

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