Jia Weikuan, Liu Mengyuan, Luo Rong, Wang Chongjing, Pan Ningning, Yang Xinbo, Ge Xinting
School of Information Science and Engineering, Shandong Normal University, Jinan, China.
Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Zhenjiang, China.
Front Plant Sci. 2022 Jun 9;13:765523. doi: 10.3389/fpls.2022.765523. eCollection 2022.
Accurate detection and segmentation of the object fruit is the key part of orchard production measurement and automated picking. Affected by light, weather, and operating angle, it brings new challenges to the efficient and accurate detection and segmentation of the green object fruit under complex orchard backgrounds. For the green fruit segmentation, an efficient YOLOF-snake segmentation model is proposed. First, the ResNet101 structure is adopted as the backbone network to achieve feature extraction of the green object fruit. Then, the C5 feature maps are expanded with receptive fields and the decoder is used for classification and regression. Besides, the center point in the regression box is employed to get a diamond-shaped structure and fed into an additional Deep-snake network, which is adjusted to the contours of the target fruit to achieve fast and accurate segmentation of green fruit. The experimental results show that YOLOF-snake is sensitive to the green fruit, and the segmentation accuracy and efficiency are significantly improved. The proposed model can effectively extend the application of agricultural equipment and provide theoretical references for other fruits and vegetable segmentation.
准确检测和分割目标果实是果园生产测量和自动采摘的关键部分。受光照、天气和操作角度的影响，在复杂的果园背景下，绿色目标果实的高效准确检测和分割面临新的挑战。针对绿色果实分割，提出了一种高效的YOLOF-snake分割模型。首先，采用ResNet101结构作为骨干网络，实现绿色目标果实的特征提取。然后，利用感受野对C5特征图进行扩展，并使用解码器进行分类和回归。此外，回归框中的中心点用于得到菱形结构，并将其输入到一个额外的深度蛇形网络中，该网络根据目标果实的轮廓进行调整，以实现绿色果实的快速准确分割。实验结果表明，YOLOF-snake对绿色果实敏感，分割精度和效率显著提高。该模型能够有效拓展农业装备的应用范围，为其他果蔬分割提供理论参考。
