College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China.
School of Engineering and Telecommunications, University of New South Wales, Sydney 2052, Australia.
Sensors (Basel). 2022 Nov 28;22(23):9270. doi: 10.3390/s22239270.
Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.
深度学习和计算机视觉的进步促使人们在农业自动化领域发现了许多解决难题的有效方法。为了提高绿色芦笋自动收获过程中的检测精度，本文提出了一种利用区域提议网络中深度信息的 DA-Mask RCNN 模型。首先，将深度残差网络和特征金字塔网络相结合形成骨干网络。其次，在 DA-Mask RCNN 模型中添加深度滤波器以辅助锚点分类中的 softmax 函数。然后，通过检测头单元进一步处理区域提议。训练和测试图像主要来自长江流域不同地区。在拍摄过程中，考虑了各种天气和光照条件，包括晴天、晴天有阴影、阴天、白天温室和夜间温室条件。使用五个构建的数据集进行性能实验、对比实验和消融实验，以验证所提出模型的有效性。使用精度、召回率和 F1 分数来评估不同方法的性能。总体实验结果表明，所提出的 DA-Mask RCNN 模型在精度和速度之间取得了很好的平衡，优于现有的算法。
