%0 Journal Article %A LI Chenghao %A ZHANG Jing %A HU Li %A XIAO Xianpeng %A ZHANG Hua %T Small Object Detection Algorithm Based on Multiscale Receptive Field Fusion %D 2022 %R 10.3778/j.issn.1002-8331.2101-0009 %J Computer Engineering and Applications %P 177-182 %V 58 %N 12 %X Aiming at the problem of low detection accuracy of general object detection algorithm in small target detection, a small object detection algorithm S-RetinaNet based on multi-scale receptive field fusion is proposed. The algorithm uses residual neural network (ResNet) to extract image features, uses recursive feature pyramid network(RFPN) to fuse features, and processes three outputs of RFPN by multiscale receptive field fusion(MRFF) to improve the ability of small target detection. Experimental results show that, compared with the original RetinaNet algorithm, the mean Average Precision(mAP) of S-RetinaNet algorithm on PASCAL VOC dataset and the average precision(AP) of MS COCO dataset are improved by 2.3 and 1.6 percentage points respectively, and the average precision small(APS) of small object detection accuracy is improved more significantly, increased by 2.7 percentage points. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2101-0009