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MT-Det: A novel fast object detector of maize tassel fromhigh-resolution imagery using single level feature

文献类型： 外文期刊

作者： Zeng, Fanguo 1 ; Ding, Ziyu 1 ; Song, Qingkui 1 ; Qiu, Guangjun 3 ; Liu, Yongxin 2 ; Yue, Xuejun 1 ;

作者机构： 1.South China Agr Univ, Coll Artificial Intelligence, Coll Elect Engn, Guangzhou 510642, Peoples R China

2.Embry Riddle Aeronaut Univ, Dept Math, Daytona Beach, FL 32114 USA

3.Guangdong Acad Agr Sci, Inst Facil Agr, Guangzhou 510640, Peoples R China

关键词： Deep learning; Object detection; Maize tassel; High-resolution imagery; High-throughput phenotyping

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 214 卷

页码：

收录情况： SCI

摘要： Accurately and quickly detecting maize tassels is crucial for successful maize breeding and seed production. However, the complex planting environment in the field, including unsynchronized growth stages, cluttered backgrounds, severe occlusions, and varying tassel sizes and shapes, presents a challenge for detection methods. Current methods either use mainstream object detectors off-the-shelf or employ shallow adaptations on the modular level, lacking a systematic design that balances both speed and accuracy. Furthermore, most studies only report evaluation results on sliced image patches instead of original high-resolution images, which can offer limited guidance for large-scale crop management. In this study, we propose a novel one-stage anchor-free maize tassel detector (MT-Det), which is based on a single level feature and designed to be simple yet effective. Extensive comparisons with both one-level counterparts and feature pyramid detectors demonstrate that MT-Det outperforms them in both detection accuracy and inference speed. To address the issue of significant accuracy drop when directly inferring on high-resolution images, we introduce a bundle of slicing-aided hyper inference technologies to our proposed MT-Det, which results in a 13% and 38% improvement of mean average precision (mAP) on proximal and unmanned aerial vehicle (UAV) high-resolution images, respectively. We believe that MT-Det provides a promising high-throughput solution for accurate and efficient detection and counting of maize tassels in real-world field conditions.

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