Automated firearms detection in cargo x-ray images using RetinaNet

We present a method for the automated detection of firearms in cargo x-ray images using RetinaNet. RetinaNet is a recently proposed powerful object detection framework that is shown to surpass the detection performance of state-of-art two-stage R-CNN family object detectors while matching the speed of one-stage object detection algorithms. We trained our models from scratch by generating training data with threat image projection (TIP) that alleviates the class imbalance problem inherent to the x-ray security inspection and eliminates the need for costly and tedious staged data collection. The method is tested on unseen weapons that are also injected into unseen cargo images using TIP. Variations in cargo content and background clutter is considered in training and testing datasets. We demonstrated RetinaNet-based firearm detection model matches the detection accuracies of traditional sliding-windows convolutional neural net firearm detectors while offering more precise object localization, and significantly faster detection speed.