The RetinaNet and Libra RetinaNet object detectors based on deep learning employ feature pyramid networks to fuse multiscale features. However, insufficient feature fusion is problematic in these detectors. In this paper, a multiscale feature fusion algorithm is proposed. The proposed algorithm is extended based on Libra RetinaNet. Two independent feature fusion modules are constructed by establishing two bottom-up paths, and the results generated by the two modules are fused with the original predicted features to improve the accuracy of the detector. The multiscale feature fusion module and Libra RetinaNet are combined to build a target detector and conduct experiments on different datasets. Experimental results demonstrate that the average accuracy of the added module detector on PASCAL VOC and MSCOCO datasets is improved by 2.2 and 1.3 percentage, respectively, compared to the Libra RetinaNet detector.