As YOLOv3, an algorithm widely used in the field of remote sensing target detection, has insufficient feature expression ability for small targets and a poor detection effect, an improved YOLOv3 algorithm for small target detection is proposed. Firstly, the global context (GC) attention mechanism is introduced, and the feature extraction network and feature pyramid networks (FPN) are improved to enhance the small-target feature extraction ability and detection ability of the model. Secondly, single-scale Retinex (SSR) fusion feature enhancement is applied to the dataset to improve the model’s learning effect of small target features. Finally, the adaptive anchor box optimization (AABO) algorithm is adopted to optimize anchors and better match anchors and targets. The experimental results on the remote sensing dataset RSOD show that the mean average precision (mAP) of the proposed algorithm is 92.5%, which is improved by 10.1% compared with that of the classic YOLOv3 algorithm, and the detection effect of small remote sensing targets is significantly improved.