Based on the deep learning method, this paper introduces a grasp detection model with the improved model Faster Region Convolutional Neural Network (Faster-RCNN). The orientation of the ground truth box in the
[25] proposed a robotic grasping detection algorithm (ROI-GD) based on the region of interest (ROI), which uses features in the region of interest to detect grasping rather than the whole scene. The algorithm is mainly divided into two stages. The first stage is to provide ROI in the ...
Faster R-CNN: Towards real-time object detection with region proposal networks. In Proc. the 29th Annual Conference on Neural Information Processing Systems, Dec. 2015, pp.91–99. Depierre A, Dellandréa E, Chen L M. Jacquard: A large scale dataset for robotic grasp detection. In Proc. ...
Ren S, He K, Girshick RB, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. CoRR arXiv:1506.01497 Ritter H, Haschke R (2015) Hands, dexterity, and the brain. In: PhD Cheng G (ed) Humanoid robotics and neuroscience: science, engineering and ...
Using FPN in a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at...
3.2.1. Improved Deformable Convolution-Based Feature Extraction Module In a recent grasp detection work, GR-ConvNet [20], based on a CNN, three convolutional layers were used for downsampling to extract features. Although the features extracted using this method have strong semantics, some details...
The adopted technique used in this system is trained on custom data and has demonstrated a high accuracy and low latency performance as it reached a detection accuracy of 96% with 96.6% correct grasp accuracy. Keywords: robot vision; deep learning; Industry 4.0; robot grasp; defect detection; ...
The adopted technique used in this system is trained on custom data and has demonstrated a high accuracy and low latency performance as it reached a detection accuracy of 96% with 96.6% correct grasp accuracy. Keywords: robot vision; deep learning; Industry 4.0; robot grasp; defect detection; ...
The adopted technique used in this system is trained on custom data and has demonstrated a high accuracy and low latency performance as it reached a detection accuracy of 96% with 96.6% correct grasp accuracy. Keywords: robot vision; deep learning; Industry 4.0; robot grasp; defect detection; ...
On the other hand, object detection focuses on pre Mask R-CNN [4], two-stage detectors for instance segmentation that are employed feature pyramid network (FPN) [16] and region proposal network (RPN) [14]. By adding a mask prediction branch, Mask R-CNN became the leading model for ...