Object detection and semantic segmentation are two of the most widely adopted deep learning algorithms in agricultural applications. One of the major sources of variability in image quality acquired in the outdoors for such tasks is changing lighting condition that can alter the appearance of the objects or the contents of the entire image. While transfer learning and data augmentation to some extent reduce the need for large amount of data to train deep neural networks, the large variety of cultivars and the lack of shared datasets in agriculture makes wide-scale field deployments difficult. In this paper, we present a high throughput robust active lighting-based camera system that generates consistent images in all lighting conditions. We detail experiments that show the consistency in images quality leading to relatively fewer images to train deep neural networks for the task of object detection. We further present results from field experiment under extreme lighting conditions where images without active lighting significantly lack to provide consistent results. The experimental results show that on average, deep nets for object detection trained on consistent data required nearly four times less data to achieve similar level of accuracy. This proposed work could potentially provide pragmatic solutions to computer vision needs in agriculture.
翻译:物体探测和语义分离是农业应用中最广泛采用的两个深层次的深层次学习算法。在户外为这种任务获得的图像质量变异的主要来源之一是改变灯光条件,改变物体的外观或整个图像的内容。在在某种程度上转移学习和数据增强工作,在某种程度上减少了对大量数据的需求,以培训深神经网络,大量品种的品种和农业缺乏共享数据集使得难以大规模实地部署。在本文中,我们展示了一个高通过量强强的动态光基照相系统,在所有照明条件下产生一致的图像。我们详细介绍了一些实验,这些实验显示图像质量的一致性,从而导致培训深神经网络进行物体探测的任务。我们进一步介绍了在极端照明条件下进行的实地实验的结果,在这些条件下,没有积极照明的图像严重缺乏提供一致的结果。实验结果表明,平均而言,接受一致数据培训的物体探测深网需要近四倍的数据才能达到类似的准确度。这一拟议工作有可能为农业的计算机视觉需求提供实用的解决办法。