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• A concise and accurate FoveaMask model is proposed for segmentation of green fruits. • A position attention module(PAM) is added into mask branch for improving robustness. • FoveaMask eliminates dependence on anchors. • It can be referred and even migrated directly to other fruit segmentation. In the process of agricultural automation production, efficient and accurate segmentation of target fruit is the basis and guarantee for numerous applications including crop growth monitoring, yield prognosis, and machine picking. Green fruit is apt to affect by complicated scenes such as occlusions and overlaps, as well as the homo-chromatic background, which brings great challenges to the recognition and segmentation. In this paper, we propose a novel methodology named FoveaMask for improving the robustness and generalization of green fruit segmentation framework. Features of input images are firstly extracted by ResNet and fused by FPN. The classification and bounding-box regression of each spatial position on the feature maps are then carried out directly by the way of full convolution. RoI Align layer is applied to fix feature region of proposals to the same size yet preserve exact spatial locations. Finally, instance-level fruit segmentation is realized by pixel-level classification on each proposal using embedded mask branches. The whole network architecture of the new model does not involve the related design and operation of anchor, which greatly improves the generalization ability for different shape fruits, alleviates the computing and storage resources, and balances the contradiction between accuracy and efficiency simultaneously. Additionally, a position attention module(PAM) is introduced into the embedding mask branch to aggregate the effective information pixels, which can improve the robustness of the segmentation model in the actual complex environment of the orchard. We test the model on green apple and immature persimmon data sets and the experimental results show that FoveaMask performs best in terms of both recognition accuracy and model complexity compared with other 11 different types of detection and segmentation models. [ABSTRACT FROM AUTHOR]

• U-Net is introduced into the green apple segmentation algorithm in complex environments. • Design an Edge structure including residual blocks and gated convolution to obtain the boundary semantic information. • Construct the ASPP structure, fusing the edge features and high-level features. Fruit segmentation is a critical step for fruit recognition, and key to the efficiency of a robotic vision system for harvesting and accurate orchard yield estimation. Due to the unstructured characteristics of orchard environment, the segmentation performance of traditional segmentation algorithms is insufficient for handling green target fruits in a complex environment. Deep learning algorithms bring a fresh perspective to target segmentation. This study proposes an ensemble U-Net segmentation model suitable for small sample datasets. Edge structures are designed by integrating residual blocks and gated convolutions to obtain the boundary semantic information of the target image; atrous convolutions are applied to resolve the contradiction between the resolution of the feature map and the receiving field, retain more multiscale context information and achieve target fruit segmentation. An atrous spatial pyramid pooling (ASPP) structure is applied to merge the edge features and the high-level features of U-Net. The experimental results show that the proposed method effectively improves the segmentation accuracy of the target fruit and the generalization ability of the model. The proposed method extends the application scope of the harvesting robot and orchard yield measurements, thereby providing a theoretical reference for other fruit and vegetable target fruit segmentation efforts. [ABSTRACT FROM AUTHOR]