In order to solve the problems of low detection performance, large number of model parameters and difficult deployment in edge devices of the existing indoor vision aided algorithm, the YOLOv7-tiny network was improved and a new YOLOv7-ghost network model was proposed. Firstly, aiming at the problem of large number of model parameters, ghost bottleneck (GB) was introduced to replace partial pooling operation and efficient layer aggregation network (ELAN) to significantly reduce the number of model parameters. Secondly, by constructing a new high-performance lightweight module (C2f-global attention module), the global and local feature information were comprehensively considered to better capture the context information of nodes. Then, spatial pyramid pooling-fast and ghost bottleneck (SPPF-GB) module were introduced to recombine and compress the features to fuse the feature information of different scales and enhance the expression ability of features. Finally, deformable convolution network (DCN) was introduced in the head part to enhance the expression ability of receptive field, so as to capture more fine-grained target structure and background information around the target. The results show that, the parameters of the improved model decrease by 20.33%, the model size decreases by 18.70%, and mean average accuracy mAP@0.50 and mAP@0.50~0.95 increases by 1.2% and 3.3%, respectively. The network model not only ensures lightweight, but also greatly improves the detection accuracy, which is more conducive to the deployment of indoor scene target detection algorithm.