CSRNet is a deep learning model designed for congested scene analysis, focusing on accurate crowd counting and high-quality density map generation. The model uses a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN as the back-end to expand the receptive field without losing resolution. CSRNet is easy to train due to its pure convolutional structure and achieves state-of-the-art performance on four datasets: ShanghaiTech, UCF_CC_50, WorldExpo'10, and UCSD. On the ShanghaiTech Part_B dataset, CSRNet achieves a 47.3% lower Mean Absolute Error (MAE) compared to previous methods. It also performs well on the TRANCOS dataset for vehicle counting, achieving a 15.4% lower MAE than the current best approach. The model's architecture includes dilated convolutional layers to maintain spatial coherence and generate accurate density maps. Experiments show that CSRNet outperforms existing methods in terms of accuracy and efficiency, demonstrating its effectiveness in both crowded and sparse scenes. The model is implemented using the Caffe framework and is evaluated using metrics such as MAE, MSE, PSNR, and SSIM. The results indicate that CSRNet provides high-quality density maps and accurate crowd counting, making it a promising solution for real-world applications.CSRNet is a deep learning model designed for congested scene analysis, focusing on accurate crowd counting and high-quality density map generation. The model uses a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN as the back-end to expand the receptive field without losing resolution. CSRNet is easy to train due to its pure convolutional structure and achieves state-of-the-art performance on four datasets: ShanghaiTech, UCF_CC_50, WorldExpo'10, and UCSD. On the ShanghaiTech Part_B dataset, CSRNet achieves a 47.3% lower Mean Absolute Error (MAE) compared to previous methods. It also performs well on the TRANCOS dataset for vehicle counting, achieving a 15.4% lower MAE than the current best approach. The model's architecture includes dilated convolutional layers to maintain spatial coherence and generate accurate density maps. Experiments show that CSRNet outperforms existing methods in terms of accuracy and efficiency, demonstrating its effectiveness in both crowded and sparse scenes. The model is implemented using the Caffe framework and is evaluated using metrics such as MAE, MSE, PSNR, and SSIM. The results indicate that CSRNet provides high-quality density maps and accurate crowd counting, making it a promising solution for real-world applications.