.png){kind=icon}
返回
Box-supervised dynamical instance segmentation for in-field cotton
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compag.2023.108390.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Crop growing information is important for precision agriculture. Instance segmentation can be employed to extract not only the location and quantity information of objects from images, but also the individual phenotype information of each target. However, when it is applied on real in-field images, complex environment increases the cost of annotating. Besides, it is hard for instance segmentation network with complicated structure to be deployed in real application scenarios. To cost-efficiently extract data used for precision management from field images, this paper proposes a novel model based on dense prediction and conditionally parameterized convolution termed Box-supervised dynamical regression instance segmentation net(BDRISNet) for segmenting individuals from image in a simple way, using box-level supervision. By formulating segmentation into dense regression from each pixel to the location of its instance indicators, mask features can be generated without redundant convolution branch. The compact construction of proposed network achieves efficient inference for segmentation. Robustness and accuracy of proposed method are evaluated on an in-field cotton boll image dataset we provide. Comparison experiments with other state-of-the-arts demonstrate that our method has an average improvement of 3.13%, 5.55%, 7.08% in AP, AP50 and AP75. Furthermore, predictions of proposed method have desirable and balanced recall and precision.
Keyword:
Cotton
Box-supervised instance segmentation
Conditionally parameterized convolution
期刊
IF:
8.9
论文数:
1.0W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
Small unopened cotton boll counting by detection with MRF-YOLO in the wild野外用mrf-yolo检测未开封的小棉铃计数
PREAI
分享收藏Automated pruning decisions in dormant sweet cherry canopies using instance segmentation使用实例分割在休眠的甜樱桃冠层中自动修剪决策
OAAI
OAAI分享收藏
查看更多内容