Flower and fruitlet thinning can be an effective method of improving the yield and quality of fruit. Automatic detection flowers and fruits at different growth stages is essential for the intelligent management of apple orchards. The further segmentation of blossom areas contributes to extracting detailed growth information of apple flowers. However, the precise detection and segmentation of blossom images is yet to be fully accomplished. An instance segmentation model which improves Mask Scoring R–CNN with a U-Net backbone (MASU R–CNN) is proposed for the detection and segmentation of apple flowers with three different levels of growth status: bud, semi-open and fully open. The foreground and background of apple flower images were combined based on the growth characteristics of apple flowers. Furthermore, 200 background images were added as background samples to form the image training dataset and a U-Net backbone was used to improve the MaskIoU head of Mask Scoring R–CNN model. This method can improve the efficiency of feature utilisation and promote the reuse of features through the concatenation of feature maps in the process of encoding and decoding. The performance of the MASU R–CNN model was verified by 100 testing images. With ResNet-101 FPN adopted as the feature extraction backbone, the precision of MASU R–CNN reached 96.43%, recall 95.37%, F1 score 95.90%, mean average precision (mAP) 0.594, and mean intersection over union (mIoU) 91.55%. The segmentation results of MASU R–CNN model outperformed those of the other state-of-the-art models.

中文翻译：

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使用改进的mask R-CNN模型对苹果花进行实例分割

疏花疏果是提高果实产量和品质的有效方法。自动检测不同生长阶段的花果对于苹果园的智能化管理至关重要。开花区域的进一步分割有助于提取苹果花的详细生长信息。然而，花朵图像的精确检测和分割尚未完全完成。提出了一种使用 U-Net 主干（MASU R-CNN）改进 Mask Scoring R-CNN 的实例分割模型，用于检测和分割具有三种不同生长状态的苹果花：芽、半开放和完全开放。根据苹果花的生长特征，组合了苹果花图像的前景和背景。此外，添加 200 张背景图像作为背景样本以形成图像训练数据集，并使用 U-Net 主干来改进 Mask Scoring R-CNN 模型的 MaskIoU 头。这种方法可以通过编码和解码过程中特征图的拼接来提高特征利用效率，促进特征的重用。MASU R-CNN 模型的性能通过 100 个测试图像进​​行了验证。采用 ResNet-101 FPN 作为特征提取主干，MASU R-CNN 的精度达到 96.43%，召回率 95.37%，F1 得分 95.90%，平均精度 (mAP) 0.594，联合平均交集 (mIoU) 91.55% . MASU R-CNN 模型的分割结果优于其他最先进模型的分割结果。