首页|Data from Wuhan University of Technology Provide New Insights into Robotics (Yol ov5s-bc: an Improved Yolov5s-based Method for Real-time Apple Detection)
Data from Wuhan University of Technology Provide New Insights into Robotics (Yol ov5s-bc: an Improved Yolov5s-based Method for Real-time Apple Detection)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Robotics have been publi shed. According to news reporting originating in Wuhan, People’s Republic of Chi na, by NewsRx journalists, research stated, “The current apple detection algorit hms fail to accurately differentiate obscured apples from pickable ones, thus le ading to low accuracy in apple harvesting and a high rate of instances where app les are either mispicked or missed altogether. To address the issues associated with the existing algorithms, this study proposes an improved YOLOv5s-based meth od, named YOLOv5s-BC, for real-time apple detection, in which a series of modifi cations have been introduced.” Financial supporters for this research include Wuhan University of Technology, N ational Innovation and Entrepreneurship Training Program for College Students.
WuhanPeople’s Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine LearningRobotRoboticsWuhan Uni versity of Technology
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