深度学习和计算机视觉的进步导致发现了许多有效的解决方案,以解决农业自动化领域的挑战性问题。为提高绿芦笋自主采收过程中的检测精度,在这篇文章中,我们提出了DA-MaskRCNN模型,它利用区域建议网络中的深度信息。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,DA-MaskRCNN模型添加了一个深度过滤器来辅助锚分类中的softmax功能。之后,区域提案由检测头单位进一步处理。训练和测试图像主要来自长江流域的不同地区。在捕获过程中,考虑了各种天气和照明条件,包括晴朗的天气,阳光明媚但黯然失色的条件,多云的天气,白天温室条件以及夜间温室条件。性能实验,比较实验,利用构建的5个数据集进行消融实验,验证了模型的有效性。Precision,召回,和F1分数值用于评估不同方法的性能。总体实验结果表明,提出的DA-MaskRCNN模型在精度和速度上的平衡优于现有算法。
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.