张远琴,肖德琴,陈焕坤,刘又夫.基于改进Faster R-CNN的水稻稻穗检测方法[J].农业机械学报,2021,52(8):231-240.
ZHANG Yuanqin,XIAO Deqin,CHEN Huankun,LIU Youfu.Rice Panicle Detection Method Based on Improved Faster R-CNN[J].Transactions of the Chinese Society for Agricultural Machinery,2021,52(8):231-240.
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基于改进Faster R-CNN的水稻稻穗检测方法   [下载全文]
Rice Panicle Detection Method Based on Improved Faster R-CNN   [Download Pdf][in English]
投稿时间:2020-09-02  
DOI:10.6041/j.issn.1000-1298.2021.08.023
中文关键词:  稻穗检测  改进Faster R-CNN  Inception_ResNet-v2  K-means
基金项目:广东省重点领域研发计划项目(2019B020214002)和广东省农业厅乡村振兴专项基金项目(5600-F19257)
作者单位
张远琴 华南农业大学 
肖德琴 华南农业大学 
陈焕坤 华南农业大学 
刘又夫 华南农业大学 
中文摘要:为了快速而准确地统计视频监测区域内的水稻穗数,提出了一种基于改进Faster R-CNN的稻穗检测方法。针对稻穗目标较小的问题,在Inception_ResNet-v2的基础上引入空洞卷积进行优化;对于不同生长期稻穗差别大的问题,设计了针对标注框尺度的K-means聚类,为候选区域生成网络提供先验知识,从而提高了检测精度。鉴于小尺寸稻穗目标的特殊性,用ROIAlign替代ROIPooling,提高了感兴趣区域的提取精度。试验测试时,根据水稻不同发育期稻穗的表型特征差异自制了3类数据集,并选取最佳聚类数为10。模型对比试验表明,本文方法的稻穗检测平均精度均值达到80.3%,较Faster R-CNN模型提升了2.4个百分点,且比SSD和YOLO系列模型有较大幅度的提升。
ZHANG Yuanqin  XIAO Deqin  CHEN Huankun  LIU Youfu
South China Agricultural University
Key Words:rice panicle detection  improved Faster R-CNN  Inception_ResNet-v2  K-means
Abstract:Rice panicle detection is the core research basis of automatic rice panicle counting and rice yield estimation. Due to the density and small size of rice panicle, the size of rice panicle varies greatly at different growth stages, which brings great challenges to the effective and accurate detection of rice panicle. In order to quickly and accurately count the number of rice panicle in the video monitoring area, a rice panicle detection method based on improved Faster R-CNN was proposed. In order to deal with the problem of small target of rice panicle,dilated convolution was introducedon the basis of Inception_ResNet-v2 to optimize the solution. For the problem that rice panicle size varied greatly in different growing periods, K-means clustering aiming at the scale of label box was designed, so as to provide prior knowledge for region proposal network and improve the detection accuracy. In addition, in view of the particularity of the detection target, ROIAlign was used instead of ROIPooling to improve the extraction accuracy of ROI. Using the Faster R-CNN as the basic network and combining the above optimization strategy, a method was proposed for rice panicle detection based on the improved Faster R-CNN. During the experimental test, three data sets were made based on the differences in phenotypic characteristics of rice panicle at different developmental stages, and selected 10 as the best cluster number feasible in practice according to the experimental results. A large number of results showed that the rice panicle detection mAP of this algorithm reached 80.3%, which was 2.4 percentage points higher than that of the original Faster R-CNN model without improved strategy. And compared with SSD and YOLO series model, it had a greater improvement.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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