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Having a tiny scale and few identifiable features, small objects are particularly difficult to detect, especially when the image resolution is not high. Further, large-scale variation across object instances places a burden on small object detection. In view of the insufficient detection ability of YOLOF (You Only Look One-level Feature) detector for small objects at low resolution, we propose an effective method, named YOLOFs, to boost small object detection precision in the case of object instances scale variation. The proposed three key modules (feature fusion module, visual perception module and feature encoding module) expand the receptive field of the network and greatly improve small object detection precision. Extensive experiments on the COCO, VOC, and Fire datasets prove the effectiveness of our method while keeping the detector simple and accurate. Without bells and whistles, our method outperforms YOLOF 5.1 AP (average precision), 6.2 AP, and 4.9 AP on small object detection at 704 × 704 , 608 × 608, and 512 × 512 resolutions, respectively, and it also achieves better performance on the VOC and Fire datasets. Our code will be made publicly available. [ABSTRACT FROM AUTHOR]