Object detection is an important research direction in the field of computer vision. To address the challenges associated with complex models and multi-scale object detection in object detection algorithms, a multi-scale feature fusion object detection algorithm based on HRNet and ASFF is proposed. Firstly, the basic module of HRNet is improved by Channel Split operation and Dwconv, and the branch structure of HRNet is improved in combination with CSPNet to reduce the number of model parameters. After improving the three branches of lightweight L-HRNet, EESP module is adopted to obtain features of different receptive field sizes, and the features are further enhanced by fusion. Secondly, the ASFF module is adopted to adaptively fuse the multi-scale features output by the EESP module. This module assigns different spatial weights to features of the three branches and adaptively fuses important spatial features. Finally, SIoU is introduced as the bounding box localization loss function, which comprehensively considers angle relationship, center point distance relationship, and shape relationship between bounding box regressions, making the loss measurement between predicted boxes and ground truth boxes more accurate . The overall number of parameters is 5.7M, achieving 85.1% mAP on the PASCAL VOC public dataset. Experimental results on MS COCO 2017 show that the mAP0.5?0.95 reaches 38.738.7%, maintaining high detection performance with fewer model parameters.