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.

翻译：机器人抓取需以适当精度实时完成，感知是这一流程的首要关键步骤。本文提出一种改进的流水线模型，旨在将不同已知或未知物体的抓取检测表示为矩形框形式，从而帮助机器人从更靠近物体适宜部位的位置启动控制程序。核心创新包括预处理、输出归一化及数据增强技术，在保证系统运行速度不降低的前提下将检测精度提升4.3%。此外，本文还对比了AlexNet、ResNet、Vgg19等经典图像特征提取预训练模型在目标检测中的表现。尽管AlexNet的复杂度低于其他模型，但其检测性能反而更优，这进一步保障了系统的实时性。