Small-target detection from images remains a challenge in the field of computer vision because of the limited size, small appearance and geometric clues, and lack of large-scale small-target datasets. To solve this issue, an adaptive feature-enhanced target detection network called YOLO-AFENet is proposed to improve the accuracy of small-target detection. First, by introducing the feature fusion factor, an improved adaptive bidirectional feature fusion module is designed using feature maps of various scales to improve the network's feature expression ability. Second, combined with the network characteristics, a spatial attention generation module is proposed to improve the network's feature localization ability by identifying the location information of the region of interest in the image. The experimental results of the UAVDT dataset show that YOLO-AFENet has a 6.3 percentage points higher average accuracy compared with YOLOv5 and is better than other target-detection networks.