基于改进YOLOX的玉米雄穗无人机遥感图像识别

doi:10.1016/j.jia.2022.09.021

Journal of Integrative Agriculture ›› 2023, Vol. 22 ›› Issue (6): 1671-1683.DOI: 10.1016/j.jia.2022.09.021

基于改进YOLOX的玉米雄穗无人机遥感图像识别

收稿日期:2022-04-11 修回日期:2022-09-27 接受日期:2022-08-26 出版日期:2023-06-20 发布日期:2022-08-26

Detection of maize tassels for UAV remote sensing image with an improved YOLOX Model

SONG Chao-yu¹, ZHANG Fan¹, LI Jian-sheng², XIE Jin-yi¹, YANG Chen¹, ZHOU Hang¹, ZHANG Jun-xiong^1#

¹ College of Engineering, China Agricultural University, Beijing 100083, P.R.China

²College of Agronomy and Biotechnology, China Agricultural University, Beijing 100083, P.R.China

Received:2022-04-11 Revised:2022-09-27 Accepted:2022-08-26 Online:2023-06-20 Published:2022-08-26
About author:SONG Chao-yu, E-mail: chaoyu@cau.edu.cn; #Correspondence ZHANG Jun-xiong, E-mail: cau2007@cau.edu.cn.
Supported by:
This research was supported by the Chinese Universities Scientific Fund (2022TC169).

摘要/Abstract

摘要： 玉米雄穗检测是玉米种植和育种农艺管理中必不可少的技术，可应用于产量估算、生长监测、智能采摘、病害检测等方面。然而，田间的玉米雄穗普遍存在遮挡现象，不同生长阶段的雄穗大小和形态颜色也不尽相同。针对这些问题，本文提出了SEYOLOX-tiny模型，可以更精准、更鲁棒地识别田间的玉米雄穗。通过无人机构建了丰富的玉米雄穗图像数据集，在保证图像质量和图像采集效率的同时兼顾不同时期的玉米雄穗的图像多样性。另外，YOLOX嵌入注意力机制，在关键特征的提取时，能有效抑制不利因素（遮挡、重叠）的噪声，有助于应对农田多变复杂的环境。实验结果显示，改进的识别算法SEYOLOX-tiny平均检测精度达到95.0%；相较于原始模型的mAP_@0.5、mAP_@0.5-0.95、mAP_{@0.5-0.95（面积=小）}和mAP_{@0.5-0.95（面积=中）}提升1.5、1.8、5.3和1.7个百分点。因此，本文提出的方法可以满足玉米穗检测视觉系统中所需要的精度和鲁棒性。

Abstract:

Maize tassel detection is essential for future agronomic management in maize planting and breeding, with application in yield estimation, growth monitoring, intelligent picking, and disease detection. However, detecting maize tassels in the field poses prominent challenges as they are often obscured by widespread occlusions and differ in size and morphological color at different growth stages. This study proposes the SEYOLOX-tiny Model that more accurately and robustly detects maize tassels in the field. Firstly, the data acquisition method ensures the balance between the image quality and image acquisition efficiency and obtains maize tassel images from different periods to enrich the dataset by unmanned aerial vehicle (UAV). Moreover, the robust detection network extends YOLOX by embedding an attention mechanism to realize the extraction of critical features and suppressing the noise caused by adverse factors (e.g., occlusions and overlaps), which could be more suitable and robust for operation in complex natural environments. Experimental results verify the research hypothesis and show a mean average precision (mAP_@0.5) of 95.0%. The mAP_@0.5, mAP_@0.5–0.95, mAP_{@0.5–0.95 (area=small)}, and mAP_{@0.5–0.95 (area=medium)} average values increased by 1.5, 1.8, 5.3, and 1.7%, respectively, compared to the original model. The proposed method can effectively meet the precision and robustness requirements of the vision system in maize tassel detection.

Key words: maize , tassel detection , remote sensing , deep learning , attention mechanism

SONG Chao-yu, ZHANG Fan, LI Jian-sheng, XIE Jin-yi, YANG Chen, ZHOU Hang, ZHANG Jun-xiong. 基于改进YOLOX的玉米雄穗无人机遥感图像识别[J]. Journal of Integrative Agriculture, 2023, 22(6): 1671-1683.

SONG Chao-yu, ZHANG Fan, LI Jian-sheng, XIE Jin-yi, YANG Chen, ZHOU Hang, ZHANG Jun-xiong. Detection of maize tassels for UAV remote sensing image with an improved YOLOX Model[J]. Journal of Integrative Agriculture, 2023, 22(6): 1671-1683.

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基于改进YOLOX的玉米雄穗无人机遥感图像识别

Detection of maize tassels for UAV remote sensing image with an improved YOLOX Model

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