To solve high computational resource requirement for running hardware platform of the series of the YOLO object detection method due to the huge parameters, the large amount of calculation, and the large scale of detection model, this paper developed a light-weight object detection network based on inverted residual structure(IR-YOLO). First, it used depth separable convolution to reduce detection model parameters and computational quantities. Secondly, it constructed inverted residual block based on depth separable convolution to extract high-dimensional feature. Finally, according to the characteristic of inverted residual structure, it used a linear activation function to reduce the information loss during the process of channels combination. The experimental results show that the IR-YOLO detection model is reduced by 47.7% compared to the YOLOv3-Tiny detection model, it validated that the IR-YOLO algorithm can effectively compress the model while maintaining detection accuracy.