Objective There are two problems in current crowd counting models. Complex heavy-weight counting models have strong counting performance but their large number of model parameters and high computational complexity result in low practicality. Current lightweight models reduce the model complexity but have poor counting performance. To address these issues a crowd counting model based on a lightweight convolutional neural network is proposed to effectively balance counting performance and counting efficiency. Methods The method consisted of two modules the feature extraction module and the density map regression module. First in the feature extraction module instead of discarding highly similar information during feature extraction as in the past more attention was paid to the fusion of intrinsic features and similar features. A lightweight linear mapping unit was designed which improved the counting accuracy while reducing network parameters and computational costs. Then multiple linear mapping units formed a lightweight linear mapping block and multiple linear mapping blocks were serially connected to form the feature extraction module. Next the features extracted by the feature extraction module were fed into the density map regression module. Instead of using a small number of standard convolutions to regress the density map the density map regression module used dilated convolutions to replace standard convolutions. Stacked dilated convolutions were utilized to increase the receptive field and a more accurate regression density map was obtained. Finally the estimated number of people was obtained by summing the regressed density map. Results The proposed method had a model size of only 0. 12 MB and a computational cost of only 9. 23 GFLOPS Giga Floating-point Operations per Second both of which were lower than those of other lightweight crowd counting models. It also achieved excellent counting performance on three crowd counting datasets the Shanghai Tech dataset the UCF-QNRF dataset and the NWPU-Crowd dataset. Conclusion The model ensures both counting performance and counting efficiency achieving the best balance between the two. It realizes real-time fast and accurate crowd counting. Compared with other lightweight crowd counting algorithms it has higher counting performance and efficiency and is more practical.