To solve the problems of high computational complexity and low detection efficiency of Transformer-based detection model,a lightweight dynamic Transformer object detection algorithm is proposed.Firstly,the dynamic gate strategy is introduced to filter important regions in the self-attention module,and a local-to-global dynamic sparse self-attention mechanism is designed,which enhances the multi-scale generalization capability of the model while reducing the computational load.Secondly,dynamic layer-skipping mechanism is introduced at the structural level of the model.Then,the model is able to adaptively adjust the parameters and structure according to the input during inference to achieve a better tradeoff between detection efficiency and accuracy.The experimental results demonstrate that the improved detection model effectively reduces the computational redundancy,which is more efficient and has a broader practical application space compared with the existing benchmark models.