CDENet is a deep learning model designed for crowd count detection tasks. It utilizes a modified VGG16 architecture with 10 layers and incorporates dilated convolutions in the backend to improve accuracy in dense crowd scenarios.
Crowd count detection is a crucial task in various domains such as urban planning, security surveillance, and event management. CDENet offers a robust solution by accurately estimating crowd density in images or videos, enabling better crowd management and analysis.
- VGG16 Architecture: CDENet is built upon the widely used VGG16 architecture, which has shown effectiveness in various computer vision tasks.
- 10-Layer Modification: To adapt VGG16 for crowd count detection, CDENet modifies the original architecture to have 10 layers, optimizing it for density estimation.
- Dilated Convolutions: In the backend layers, CDENet incorporates dilated convolutions to capture contextual information over larger receptive fields, improving accuracy, especially in densely packed crowd scenarios.
- Deep Learning Framework: CDENet is implemented using popular deep learning frameworks such as TensorFlow or PyTorch, allowing for easy integration into existing workflows.
- Pre-Trained Weights: Pre-trained weights are available, facilitating transfer learning for crowd count detection tasks with limited labeled data.
Contributions to CDENet are welcome! If you have suggestions for improvements, bug fixes, or new features, please open an issue or submit a pull request.
CDENet is licensed under the MIT License. You are free to use, modify, and distribute the code for both commercial and non-commercial purposes.