首页|New Robotics Data Have Been Reported by Researchers at Harbin Engineering Univer sity (Real-time and Accurate Meal Detection for Meal-assisting Robots)
New Robotics Data Have Been Reported by Researchers at Harbin Engineering Univer sity (Real-time and Accurate Meal Detection for Meal-assisting Robots)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Robotics are disc ussed in a new report. According to news originating from Harbin, People’s Repub lic of China, by NewsRx correspondents, research stated, “Meal detection is an i mportant technology to ensure success rate of meal -assisting robotics. However, due to the strong interclass similarity and intraclass variability presented by appearance, gesture, and complex traits of meals in different scenarios, it is more challenging to real-time and accurate detect meals.” Funders for this research include National Key R & D Program of Ch ina, Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from Harbin Engineering University, “To address the above problems, a novel method based on deformable c onvolution and CloFormer (CF) transformer to optimize the YOLOv8s was proposed t o achieve efficient and accurate detection for meal. The YOLOv8s model architect ure was enhanced by introducing deformable convolution to capture finer -grained spatial information, and the CloFormer module was introduced to capture high -f requency local and low -frequency global information through shared weights and context -aware weights, we notated it as DCF-YOLOv8s. The proposed method was ev aluated on meal datasets, which were evaluated separately with baseline model an d several state-of-the-art (SOTA) detection models, and results show that the pr oposed method achieves better performance. Specifically, the proposed method can achieve 88.5% mean average accuracy (mAP) at 43.6 frames per seco nd (FPS), validating its efficiency and accuracy in meal detection for meal -ass isting robotics. The effectiveness of introducing deformable convolution and Clo Former modules was verified by ablation experiments, and validating the importan ce of adopting data augmentation methods.”
HarbinPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRoboticsHarbin Engineer ing University
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