Increasing shape bias to improve the precision of center pivot irrigation system detection

Abstract

Irrigation is indispensable in agriculture. Center pivot irrigation systems are popular means of irrigation since they are water-efficient and labor-saving. Monitoring center pivot irrigation systems provides important information for the understanding of agricultural production, water resources consumption and environmental change. Deep learning has become an effective approach for object detection and semantic segmentation. Recent studies have shown that convolutional neural networks (CNNs) are prone to be texture-biased rather than shape-biased, and increasing shape bias can improve the robustness and performance of CNNs. In this study, a simple yet effective method was proposed to increase shape bias in object detection networks to improve the precision of center pivot irrigation system detection. We extracted edge images of training samples and integrated them into the training data to increase shape bias in the networks. With the proposed shape increasing training scheme, we evaluated and compared PVANET and YOLOv4. Experiments with the images in Mato Grosso have shown that both PVANET and YOLOv4 achieved improved performance, which demonstrated the validity of the proposed method.