This paper aims at the problems of small target proportion, complex background and low detection accuracy in aerial images. Proposes a small aerial target detection algorithm based on receptive field enhancement and parallel coordinate attention. A receptive field enhancement module is designed to expand the receptive field range by using cavity convolution of different sizes and integrate effective channel attention mechanism to improve the feature extraction ability of the network. The feature fusion structure is improved to improve the detection ability of small targets. A parallel coordinate attention module is designed to improve the ability of aerial photography dense small target detection and anti-background interference. Experiments are conducted on VisDrone dataset with different input resolutions. The experimental results show that mAP0.5 and mAP0.5:0.95 of the proposed algorithm are improved by 5.4% and 4.2% compared with YOLOv5 algorithm, and mAP0.5 can reach 54.9% at input resolution 1 536×1 536. It can achieve better effect of small target detection.