Abstract: For the problem of low computing power of terminal equipment and high demand for the response speed of detection algorithms in the industrial application field,a lightweight real-time object detection algorithm based on split inverted residuals is proposed.Firstly,the split inverted residuals are used in the backbone network to reduce the number of parameters and calculations to achieve faster inference. Secondly, the self-adaptive context-awareness module and the lightweight two-way feature fusion module are introduced to improve the feature information exchange and increase the detection performance of small targets while avoiding the addition of learning parameters and inference cost. According to the results of experiments, the algorithm has a detection accuracy of 21.1% in the MS COCO data set and a detection speed of 48frame/s, far exceeding the comparison when the parameter amount is only 0.75M. The detection algorithm is more suitable for object detection tasks on mobile devices which can not provide high computing power.