首页|New Findings on Robotics from Bannari Amman Institute of Technology Summarized ( Implementation of Hand-object Pose Estimation Using Ssd and Yolov5 Model for Obj ect Grasping By Scara Robot)
New Findings on Robotics from Bannari Amman Institute of Technology Summarized ( Implementation of Hand-object Pose Estimation Using Ssd and Yolov5 Model for Obj ect Grasping By Scara Robot)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics are presented i n a new report. According to news reporting out of Sathyamangalam, India, by New sRx editors, research stated, “Enforcement of advanced deep learning methods in hand-object pose estimation is an imperative method for grasping the objects saf ely during the human-robot collaborative tasks. The position and orientation of a hand-object from a two-dimensional image is still a crucial problem under vari ous circumstances like occlusion, critical lighting, and salient region detectio n and blur images.” Our news journalists obtained a quote from the research from the Bannari Amman I nstitute of Technology, “In this paper, the proposed method uses an enhanced Mob ileNetV3 with single shot detection (SSD) and YOLOv5 to ensure the improvement i n accuracy and without compromising the latency in the detection of hand-object pose and its orientation. To overcome the limitations of higher computation cost , latency and accuracy, the Network Architecture Search and NetAdapt Algorithm i s used in MobileNetV3 that perform the network search for parameter tuning and a daptive learning for multiscale feature extraction and anchor box offset adjustm ent due to auto-variance of weight in the level of each layers. The squeeze-and- excitation block reduces the computation and latency of the model. Hard-swish ac tivation function and feature pyramid networks are used to prevent over fitting the data and stabilizing the training. Based on the comparative analysis of Mobi leNetV3 with its predecessor and YOLOV5 are carried out, the obtained results ar e 92.8% and 89.7% of precision value, recall value o f 93.1% and 90.2%, mAP value of 93.3% a nd 89.2%, respectively.”
SathyamangalamIndiaAsiaEmerging Te chnologiesMachine LearningRobotRoboticsBannari Amman Institute of Techno logy
NETL
NSTL
