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.

翻译：本文旨在通过实现与分析多种网络模型，评估其在边缘设备应用（如X射线威胁检测）中的适配性。本研究采用先进的YOLO目标检测模型解决安检行李图像中的威胁检测任务，设计并研究了三种模型：Tiny YOLO、QCFS Tiny YOLO及SNN Tiny YOLO。我们在Tiny YOLOv7模型中采用一种替代激活函数，该函数经计算可实现与脉冲激活函数激活过程的零期望转换误差。这种Tiny YOLO的\textit{QCFS}版本复现了超低延迟、高效率SNN架构的激活函数，在开源X射线威胁检测数据集CLCXray上达到了最优性能。此外，我们还研究了脉冲Tiny YOLO在同一X射线威胁检测数据集上的行为特性。