Enhancing 2D object detection by optimizing anchor generation and addressing class imbalance.

This project uses the TensorFlow Object Detection API and proposes several modifications to the standard Faster R-CNN implementation, improving 2D detection over the Waymo Open Dataset:

• Per-region anchor optimization using genetic algorithms
• Spatial ROI features in the second-stage Fast R-CNN header network
• Reduced focal loss to improve performance over minority and difficult instances
• Ensemble models using non-maximum suppression
  
  

This project depends on several libraries and contains two submodules that have to be installed:

• TensorFlow Object Detection API: The models folder contains a forked repository with the proposed modifications
• Waymo repository for reading the dataset and computing metrics

Full details on the installation steps and system requirements can be found at installation.md

The scripts folder provides ready-to-use shell scripts for many operations:

• Convert Waymo dataset to the format required by the TF Object Detection API
• Training models
• Exporting inference graphs
• Infer predictions
• Calculating inference time
• Ensemble predictions
• Computing Waymo metrics

An example Faster R-CNN model configuration is provided in the file pipeline.config, using the proposed improvements: anchor optimization, spatial ROI features, and reduced focal loss

• Pedro Lara-Benítez - LinkedIn
• Manuel Carranza-García - LinkedIn
• Jorge García-Gutiérrez
• José C. Riquelme

This project is licensed under the MIT License - see the LICENSE.md file for details