A light-weight, real-time approach named RTGN (real-time grasp net) was proposed to improve the accuracy and speed of robotic grasp detection for novel objects of diverse shapes, types and sizes. Firstly, a multi-scale dilated convolution module was designed to construct a light-weight feature extraction backbone. Secondly, a mixed attention module was designed to help the network focus more on meaningful features. Finally, the pyramid pool module was deployed to fuse the multi-level features extracted by the network, thereby improving the capability of grasp perception to the object. On the Cornell grasping dataset, RTGN generated grasps at a speed of 142 frame per second and attained accuracy rates of 98.26% and 97.65% on image-wise and object-wise splits, respectively. In real-world robotic grasping experiments, RTGN obtained a success rate of 96.0% in 400 grasping attempts across 20 novel objects. Experimental results demonstrate that RTGN outperforms existing methods in both detection accuracy and detection speed. Furthermore, RTGN shows strong adaptability to variations in the position and pose of grasped objects, effectively generalizing to novel objects of diverse shapes, types and sizes.