Aiming at the problem of poor recognition effect due to dense occlusion of the target to be recognized during visual grasping of service robots， we propose to improve the dense occlusion target recognition method for service robots with YOLOv7. First， in order to improve the problem of recognition difficulties caused by the loss of feature information of densely occluded targets， a deep over-parameterized convolution was used to construct a deep over-parameterized high-efficiency aggregation network， and different convolution kernels were used to operate on each channel to enhance the network sensing ability， so that the network focused on the features of the target's uncovered area； second， in order to suppress the influence caused by dense occlusions and indistinguishable target boundaries on recognition， the coordinate attention mechanism was embedded into the backbone network. This enabled the network to obtain target position information and paid more attention to the important areas in the feature map， thereby enhancing the capability of the network to extract features； finally， the Ghost network was used to improve the lightweighting， reduce the number of parameters of the network model and the number of floating-point operations to realize the lightweighting， reduce the memory occupation of the model， and increase the model operation efficiency. Comparison experiments were conducted on the model in the self-constructed dataset and the public dataset respectively， and the experimental results show that the improved model achieves a mAP of 92.9% on the self-constructed dataset and 87.8% on the public dataset， which is better than the original method and the other commonly used methods. In this paper， the model reduces the memory footprint while the recognition accuracy and recognition efficiency are improved， and the overall performance is better.