用深度学习自动检测玉米穗并估算方向，省时又准。
Corn Ear Detection and Orientation Estimation Using Deep Learning
- 用目标检测+关键点识别算法定位玉米穗
- 90%穗能被准确检测，方向误差均值18度
- 适合农业科研与智能育种人群使用
监测玉米植株的生长行为，如果穗发育，可为植物健康与生长提供关键信息。传统上，果穗角度测量依赖人工，耗时且易出错。本文提出一种基于计算机视觉的系统，用于在图像序列中检测与追踪玉米果穗。该系统可准确检测、追踪并预测果穗方向，显著节省人工时间，并拓展果穗方向研究可能性，提升玉米生产效率。采用结合目标检测与关键点检测的算法，可检测90%的果穗；方向估计的平均绝对误差（MAE）为18度，优于两人手工测量间平均15度的差异。结果表明，计算机视觉技术可用于玉米生长监测，推动该领域进一步研究。
原文摘要 · Abstract (English)
Monitoring growth behavior of maize plants such as the development of ears can give key insights into the plant's health and development. Traditionally, the measurement of the angle of ears is performed manually, which can be time-consuming and prone to human error. To address these challenges, this paper presents a computer vision-based system for detecting and tracking ears of corn in an image sequence. The proposed system could accurately detect, track, and predict the ear's orientation, which can be useful in monitoring their growth behavior. This can significantly save time compared to manual measurement and enables additional areas of ear orientation research and potential increase in efficiencies for maize production. Using an object detector with keypoint detection, the algorithm proposed could detect 90 percent of all ears. The cardinal estimation had a mean absolute error (MAE) of 18 degrees, compared to a mean 15 degree difference between two people measuring by hand. These results demonstrate the feasibility of using computer vision techniques for monitoring maize growth and can lead to further research in this area.
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