Improving the Successful Robotic Grasp Detection Using Convolutional
  Neural Networks
Hamed Hosseini Mehdi Tale Masouleh Ahmad Kalhor
Robotic grasp should be carried out in a real-time manner by proper accuracy.
Perception is the first and significant step in this procedure. This paper
proposes an improved pipeline model trying to detect grasp as a rectangle
representation for different seen or unseen objects. It helps the robot to
start control procedures from nearer to the proper part of the object. The main
idea consists in pre-processing, output normalization, and data augmentation to
improve accuracy by 4.3 percent without making the system slow. Also, a
comparison has been conducted over different pre-trained models like AlexNet,
ResNet, Vgg19, which are the most famous feature extractors for image
processing in object detection. Although AlexNet has less complexity than other
ones, it outperformed them, which helps the real-time property.