首页|Researchers' from Yancheng Teachers University Report Details of New Studies and Findings in the Area of Robotics (High-precision apple recognition and localiza tion method based on RGB-D and improved SOLOv2 instance segmentation)
Researchers' from Yancheng Teachers University Report Details of New Studies and Findings in the Area of Robotics (High-precision apple recognition and localiza tion method based on RGB-D and improved SOLOv2 instance segmentation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on robotics have been published. According to news originating from Yancheng, People's Republic of China, by NewsRx correspondents, research stated, "Intelligent applepicking robots can significantly improve the efficiency of apple picking, and the realiz ation of fast and accurate recognition and localization of apples is the prerequ isite and foundation for the operation of picking robots. Existing apple recogni tion and localization methods primarily focus on object detection and semantic s egmentation techniques." The news reporters obtained a quote from the research from Yancheng Teachers Uni versity: "However, these methods often suffer from localization errors when faci ng occlusion and overlapping issues. Furthermore, the few instance segmentation methods are also inefficient and heavily dependent on detection results. Therefo re, this paper proposes an apple recognition and localization method based on RG B-D and an improved SOLOv2 instance segmentation approach. To improve the effici ency of the instance segmentation network, the EfficientNetV2 is employed as the feature extraction network, known for its high parameter efficiency. To enhance segmentation accuracy when apples are occluded or overlapping, a lightweight sp atial attention module is proposed. This module improves the model position sens itivity so that positional features can differentiate between overlapping object s when their semantic features are similar. To accurately determine the apple-pi cking points, an RGB-D-based apple localization method is introduced. Through co mparative experimental analysis, the improved SOLOv2 instance segmentation metho d has demonstrated remarkable performance. Compared to SOLOv2, the F1 score, mAP , and mIoU on the apple instance segmentation dataset have increased by 2.4, 3.6 , and 3.8%, respectively. Additionally, the model's Params and FLOP s have decreased by 1.94M and 31 GFLOPs, respectively."
Yancheng Teachers UniversityYanchengPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano- robotRobotics
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