Explicitly incorporating spatial information to recurrent networks for agriculture

TL;DR

This paper introduces a novel method that explicitly incorporates spatial and temporal information into recurrent neural networks to enhance agricultural image classification, leveraging RGB-D data and odometry for improved accuracy and robustness.

Contribution

It presents a new approach combining spatial-temporal data fusion with recurrent models, significantly improving agricultural image segmentation performance.

Findings

Achieved 4.7% IoU improvement in crop-weed segmentation

Achieved 2.6% IoU improvement in fruit segmentation

Demonstrated robustness to variable framerates and odometry errors

Abstract

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.