This paper proposes an improved object detection algorithm based on a dynamically deformable convolutional network (D-DCN), aiming to solve the multi-scale and variability challenges in object detection tasks. First, we review traditional methods in the field of object detection and introduce the current research status of improved methods based on multi-scale and variability convolutional neural networks. Then, we introduce in detail our proposed improved algorithms, including an improved feature pyramid network and a dynamically deformable network. In the improved feature pyramid network, we introduce a multi-scale feature fusion mechanism to better capture target information at different scales. In dynamically deformable networks, we propose dynamic offset calculations and dynamic convolution operations to achieve dynamic adaptation to the target shape and pose. We also validate our method by conducting experiments on the datasets KITTI and Caltech. Finally, we design a comprehensive loss function that considers both location localization error and category classification error to guide model training. Experimental results show that our improved algorithm achieves significant performance improvements in target detection tasks, with higher accuracy and robustness compared with traditional methods. Our work provides an effective method to solve the multi-scale and variability challenges in target detection tasks and has high practical value and prospects for general application.