Human head detection is an important research subject for counting people and is often considered to be a useful approach for video monitoring. The challenges associated with human head detection include instance occlusion, background interference, and uneven illumination; this study aims to address these challenges through a method based on the regional fully convolutional neural network. Initially, in the feature learning stage, features are acquired using a residual network (ResNet), and the region of interest is obtained through regional proposal networks. Subsequently, a deformable convolution layer is added into ResNet, and the region of interest is provided as input into the pooling layer for deformable position-sensitive mean pooling. Finally, the target location is classified and refined along with the alignment of the proposed position-sensitive region of interest to complete the pooling operation. Further, the anchor generation rules in regional proposal networks are updated to improve the detection effect of the network based on multi-scale head. The detection ability of head targets under complex tasks is improved using an online hard sample mining algorithm; subsequently, the mutual interference between the bounding boxes is reduced by the soft non-maximum suppression. After applying the proposed method to the HollywoodHeads dataset, the average recognition accuracy is confirmed to become 83.24%, which is better than those of other methods in the current literature.