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.

提出了一种改进的流水线模型，用于检测不同可见或不可见对象的矩形表示，以实现机器人的实时掌握，通过预处理、输出归一化和数据增强来提高准确性，并对 AlexNet、ResNet 和 Vgg19 等不同预训练模型进行了比较。尽管 AlexNet 比其他模型复杂度更低，但它表现出色，有助于实现实时性。