Aimed at the problem that the CenterNet detection algorithm has a large number of network parameters and fails to fully and effectively utilize the multi-scale local region features, an MIR-SPPA-CenterNet detection method is proposed to improve the CenterNet detection network. Specifically, mixed invert residual (MIR) block was introduced into the backbone network of CenterNet to achieve a lightweight effect. In addition, an improved spatial pyramid pooling with attention (SPPA) block was introduced to pool, cascade, and filter multi-scale local area features so that the network could adaptively learn more comprehensive and effective target features. Experiments show that this method has better detection results on the general PASCAL VOC dataset and the self-built L-KITTI dataset.