Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation

The cucumber fruits have the same color with leaves and their shapes are all long and narrow, which is different from other common fruits, such as apples, tomatoes, and strawberries, etc. Therefore, cucumber fruits are more difficult to be detected by machine vision in greenhouses for special color...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.139635-139642
Main Authors: Liu, Xiaoyang, Zhao, Dean, Jia, Weikuan, Ji, Wei, Ruan, Chengzhi, Sun, Yueping
Format: Article
Language:English
Subjects:
Anchors
Artificial neural networks
Aspect ratio
Boxes
Color
cucumber detection
Feature extraction
Fruits
Greenhouses
Image color analysis
Image segmentation
Instance segmentation
Machine vision
Mask RCNN
Object segmentation
Pixels
Shape
Strawberries
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Summary:The cucumber fruits have the same color with leaves and their shapes are all long and narrow, which is different from other common fruits, such as apples, tomatoes, and strawberries, etc. Therefore, cucumber fruits are more difficult to be detected by machine vision in greenhouses for special color and shape. A pixel-wise instance segmentation method, mask region-based convolutional neural network (Mask RCNN) of an improved version, is proposed to detect cucumber fruits. Resnet-101 is selected as the backbone of Mask RCNN with feature pyramid network (FPN). To improve the detection precision, region proposal network (RPN) in original Mask RCNN is improved. Logical green (LG) operator is designed to filter non-green background and limit the range of anchor boxes. Besides, the scales and aspect ratios of anchor boxes are also adjusted to fit the size and shape of fruits. Improved Mask RCNN has a better performance on test images. The test results are compared with that of original Mask RCNN, Faster RCNN, you only look once (YOLO) V2 and YOLO V3. The F 1 score of improved Mask RCNN in test results reaches 89.47%, which is higher than the other methods. The average elapsed time of improved Mask RCNN is 0.3461 s, which is only lower than the original Mask RCNN. Meanwhile, the mean value and standard deviation of location deviation in improved Mask RCNN are 2.10 pixels and 1.73 pixels respectively, which are lower than the other methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2942144