  This paper implements and analyzes multiple networks with the goal of
understanding their suitability for edge device applications such as X-ray
threat detection. In this study, we use the state-of-the-art YOLO object
detection model to solve this task of detecting threats in security baggage
screening images. We designed and studied three models - Tiny YOLO, QCFS Tiny
YOLO, and SNN Tiny YOLO. We utilize an alternative activation function
calculated to have zero expected conversion error with the activation of a
spiking activation function in our Tiny YOLOv7 model. This \textit{QCFS}
version of the Tiny YOLO replicates the activation function from ultra-low
latency and high-efficiency SNN architecture. It achieves state-of-the-art
performance on CLCXray, an open-source X-ray threat Detection dataset. In
addition, we also study the behavior of a Spiking Tiny YOLO on the same X-ray
threat Detection dataset.
