دورية أكاديمية
Citrus Tree Crown Segmentation of Orchard Spraying Robot Based on RGB-D Image and Improved Mask R-CNN
العنوان: | Citrus Tree Crown Segmentation of Orchard Spraying Robot Based on RGB-D Image and Improved Mask R-CNN |
---|---|
المؤلفون: | Peichao Cong, Jiachao Zhou, Shanda Li, Kunfeng Lv, Hao Feng |
المصدر: | Applied Sciences, Vol 13, Iss 1, p 164 (2022) |
بيانات النشر: | MDPI AG, 2022. |
سنة النشر: | 2022 |
المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
مصطلحات موضوعية: | citrus tree crown segmentation, variable spraying, mask region-based convolutional neural network, squeeze-and-excitation residual network, UNet++, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
الوصف: | Orchard spraying robots must visually obtain citrus tree crown growth information to meet the variable growth-stage-based spraying requirements. However, the complex environments and growth characteristics of fruit trees affect the accuracy of crown segmentation. Therefore, we propose a feature-map-based squeeze-and-excitation UNet++ (MSEU) region-based convolutional neural network (R-CNN) citrus tree crown segmentation method that intakes red–green–blue-depth (RGB-D) images that are pixel aligned and visual distance-adjusted to eliminate noise. Our MSEU R-CNN achieves accurate crown segmentation using squeeze-and-excitation (SE) and UNet++. To fully fuse the feature map information, the SE block correlates image features and recalibrates their channel weights, and the UNet++ semantic segmentation branch replaces the original mask structure to maximize the interconnectivity between feature layers, achieving a near-real time detection speed of 5 fps. Its bounding box (bbox) and segmentation (seg) AP50 scores are 96.6 and 96.2%, respectively, and the bbox average recall and F1-score are 73.0 and 69.4%, which are 3.4, 2.4, 4.9, and 3.5% higher than the original model, respectively. Compared with bbox instant segmentation (BoxInst) and conditional convolutional frameworks (CondInst), the MSEU R-CNN provides better seg accuracy and speed than the previous-best Mask R-CNN. These results provide the means to accurately employ autonomous spraying robots. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 2076-3417 |
Relation: | https://www.mdpi.com/2076-3417/13/1/164; https://doaj.org/toc/2076-3417 |
DOI: | 10.3390/app13010164 |
URL الوصول: | https://doaj.org/article/aaf8f4f160c74b739710241cd87df8ba |
رقم الأكسشن: | edsdoj.f8f4f160c74b739710241cd87df8ba |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 20763417 |
---|---|
DOI: | 10.3390/app13010164 |