The existing general detection methods still have the problem of high missing rate in small target detection. To improve the detection rate of the head, the ResNet DenseNet MDC (Mixed Dilated Convolution) YOLOv3 (RDM-YOLOv3) target detection network is proposed on the basis of YOLOv3. Firstly, the feature extraction network DarkNet-53 of YOLOv3 is improved, and a feature extraction network RD-Net based on ResNet and DenseNet is proposed to extract more semantic information. Then, a mixed dilated convolution structure is constructed by sampling the feature layers using dilated convolution with different dilated rates to improve the sensitivity to small targets. Using RDM-YOLOv3 to compare with other methods on Brainwash dataset and HollywoodHeads dataset, the AP (Average Precision) values reached 93.1% and 86.8%, respectively. The experimental results are better than that of other methods, and the performance of small target detection is significantly improved.