In this paper, we proposed an improved YOLOv4 algorithm model to solve the problems of low detection accuracy and slow detection speed of traditional indoor scene object detection methods. First, we constructed an indoor scene object detection dataset. Then, we applied the K-means++ clustering algorithm to optimize the parameters of the priori box and improve the matching degree between the priori box and object. Next, we adjusted the network structure of the original YOLOv4 model and integrated the cross stage partial network architecture into the neck network of the model. This eliminates the gradient information redundancy phenomenon caused by the gradient backpropagation in the feature fusion stage and improves the detection ability for indoor targets. Furthermore, we introduced a depthwise separable convolution module to replace the original 3×3 convolution layer in the model to reduce the model parameters and improve the detection speed. The experimental results show that the improved YOLOv4 algorithm achieves an average accuracy of 83.0% and a detection speed of 72.1 frame/s on the indoor scene target detection dataset, which is 3.2 percentage points and 6 frame/s higher than the original YOLOv4 algorithm, respectively, additionally, the model size is reduced by 36.3%. The improved YOLOv4 algorithm outperforms other indoor scene object detection algorithms based on deep learning.