WH-DETR: An Efficient Network Architecture for Wheat Spike Detection in Complex Backgrounds

Zhenlin Yang, Wanhong Yang, Jizheng Yi () and Rong Liu
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Zhenlin Yang: College of Computer Science and Mathematics, Central South University of Forestry and Technology, Changsha 410004, China
Wanhong Yang: College of Computer Science and Mathematics, Central South University of Forestry and Technology, Changsha 410004, China
Jizheng Yi: College of Advanced Interdisciplinary Studies, Central South University of Forestry and Technology, Changsha 410004, China
Rong Liu: College of Advanced Interdisciplinary Studies, Central South University of Forestry and Technology, Changsha 410004, China

Agriculture, 2024, vol. 14, issue 6, 1-22

Abstract: Wheat spike detection is crucial for estimating wheat yields and has a significant impact on the modernization of wheat cultivation and the advancement of precision agriculture. This study explores the application of the DETR (Detection Transformer) architecture in wheat spike detection, introducing a new perspective to this task. We propose a high-precision end-to-end network named WH-DETR, which is based on an enhanced RT-DETR architecture. Initially, we employ data augmentation techniques such as image rotation, scaling, and random occlusion on the GWHD2021 dataset to improve the model’s generalization across various scenarios. A lightweight feature pyramid, GS-BiFPN, is implemented in the network’s neck section to effectively extract the multi-scale features of wheat spikes in complex environments, such as those with occlusions, overlaps, and extreme lighting conditions. Additionally, the introduction of GSConv enhances the network precision while reducing the computational costs, thereby controlling the detection speed. Furthermore, the EIoU metric is integrated into the loss function, refined to better focus on partially occluded or overlapping spikes. The testing results on the dataset demonstrate that this method achieves an Average Precision (AP) of 95.7%, surpassing current state-of-the-art object detection methods in both precision and speed. These findings confirm that our approach more closely meets the practical requirements for wheat spike detection compared to existing methods.

Keywords: deep learning; detection transformer; feature pyramid; wheat spike detection; agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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