Current grasp detection algorithms suffer from the poor accuracy and time-consuming or expensive data annotation in densely occluded scenes. To address this concern, a step-by-step improved solution for object detection and grasp detection based on RGB-D fusion is proposed. The grasp detection model trained on single-object can be directly applied to densely occluded multi-object scenes. Firstly, considering the multi-scale characteristics of objects in densely occluded scenes, the sub-stage and path aggregation(SPA) multi-scale feature fusion module is proposed to enrich the high dimensional feature characterization of middle fusion detector SPA-YOLO-Fusion, so as to locate all objects. Then the GR-ConvNet equipped with RGB-D pixel-level fusion outputs the optimal grasp points of all detected objects. At the same time, the background padding preprocessing algorithm is proposed to reduce the interference of other objects in the GR-ConvNet. The mAP of SPA-YOLO-Fusion is 10% and 7% higher than that of YOLOv3-tiny and YOLOv4-tiny on the LineMOD dataset, respectively. The grasp detection accuracy of the GR-ConvNet equipped with the padding algorithm is improved by 23% compared with the original model on the YODO_Grasp dataset, which is collected from the actual scene.