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Instance segmentation of apple flowers using the improved mask R–CNN model

Tian, Yunong, Yang, Guodong, Wang, Zhe, Li, En, Liang, Zize
Biosystems engineering 2020 v.193 pp. 264-278
Malus domestica, apples, automatic detection, computer vision, data collection, developmental stages, flowers, fruit quality, fruits, orchards
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.