State-of-art object detection networks like YOLO, SSD and Faster R-CNN all have achieved great success in object detection. However, these algorithms have a low performance in small object detection. So, we produce the Expanding receptive field YOLO (ERF-YOLO) to deal with this problem. At first, we propose an efficient block which is called expanding receptive field block (ERF-block) to capture more information in larger areas. Base on YOLOv2, we down-sample the low-level location information by ERF-block, and up-sample feature information by deconvolution. Then we further assemble these two parts together to make the prediction. After training the network on VOC dataset, we have a good result with 82.6% mAP (mean Average Precision) which is 4.0% higher than the original YOLOv2 network. Thanks to the efficient block, it takes 62fps to detect one image when the input size is 416×416, which could keep a real-time speed. In addition, we also evaluate the model on a remote sensing dataset which contains many small targets, and it also shows that ours model has a better performance.

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