论文标题
明确将空间信息纳入农业的经常性网络
Explicitly incorporating spatial information to recurrent networks for agriculture
论文作者
论文摘要
在农业中，大多数视觉系统执行静止图像分类。然而，最近的工作强调了空间和时间提示作为改善分类绩效的丰富信息来源的潜力。在本文中，我们提出了新的方法来明确捕获空间和时间信息，以改善深卷积神经网络的分类。我们利用可用的RGB-D图像和机器人探光仪来执行框架间特征图空间注册。然后将这些信息融合在经常学习的模型中，以提高其准确性和鲁棒性。我们证明，这可以大大提高分类性能，而我们的最佳性能时空模型（ST-ATTE）可实现4.7的交互作用（IOU [％]）的绝对性能改进，用于质量质量分割，水果（甜椒）分割的2.6。此外，我们表明这些方法对可变的帧和探针误差是可靠的，这些方法经常在现实世界应用中观察到。
In agriculture, the majority of vision systems perform still image classification. Yet, recent work has highlighted the potential of spatial and temporal cues as a rich source of information to improve the classification performance. In this paper, we propose novel approaches to explicitly capture both spatial and temporal information to improve the classification of deep convolutional neural networks. We leverage available RGB-D images and robot odometry to perform inter-frame feature map spatial registration. This information is then fused within recurrent deep learnt models, to improve their accuracy and robustness. We demonstrate that this can considerably improve the classification performance with our best performing spatial-temporal model (ST-Atte) achieving absolute performance improvements for intersection-over-union (IoU[%]) of 4.7 for crop-weed segmentation and 2.6 for fruit (sweet pepper) segmentation. Furthermore, we show that these approaches are robust to variable framerates and odometry errors, which are frequently observed in real-world applications.
