Kaggle Competitions
Passenger Screening Algorithm Challenge
About the Challenge: The Department of Homeland Security (DHS) gives notice of the availability of the “Department of Homeland Security’s Person Screening Algorithm Challenge Prize Competition and rules.” The DHS Science and Technology Directorate ( S&T) Homeland Security Advanced Research Projects Agency (HSARPA) Explosives Division (EXD) and the Transportation Security Administration (TSA) (Competition Sponsor) are seeking new automated detection algorithms from individuals and entities that improve the speed, accuracy, and detection of small threat objects and other prohibited items during the airport passenger screening process. Algorithms developed from this Competition, or through further research and development under a limited intellectual property use agreement, have the potential to improve the speed, the detection of prohibited items, and the accuracy of Advanced Imaging Technology (AIT) scanners. A comprehensive set of new automated detection algorithms have the potential to be integrated into the latest screening equipment.
Models idle_speculation used to win
- Multi-view CNN (MVCNN) for shape recognition
- LightGBM - gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification, etc.
- ImageNet: VGGNet, ResNet, Inception, and Xception with Keras
- ImageNet | Wikipedia
- ResNet50 | TensorFlow Keras
- What is the (Visual Geometry Group) VGG neural network?
- VGG is a convolutional neural network in TensorFlow
- TensorFlow VGG Pretrained | Aditya Ardiya on Kaggle
- VGG-16 Pre-trained Model for Keras | Karen Simonyan, Andrew Zisserman
- Visual Geometry Group
