A multi-modal deep neural network grasping detection network was proposed to address the issue of low accuracy in robot grasping detection tasks for unknown objects. Firstly, residual modules were introduced in both RGB and depth channels to further enhance the feature extraction capability of the network. Secondly, a multimodal feature fusion module was introduced for feature fusion. Finally, the best grasping detection result was obtained by fusing features through fully connected layer regression. The experimental results demonstrate that the algorithm proposed achieves a precision rate of 95.7% for grasping and 94.6% for object segmentation on the Cornell dataset. In addition, it has been demonstrated through ablation experiments that introducing residual modules can improve the performance of network crawling detection.