This paper implements and analyses multiple nets to determine their suitability for edge devices to solve the problem of detecting Threat Objects from X-ray security imaging data. There has been ongoing research on applying Deep Learning techniques to solve this problem automatedly. We utilize an alternative activation function calculated to have zero expected conversion error with the activation of a spiking activation function, in the our tiny YOLOv7 model. This QCFS version of the tiny YOLO replicates the activation of ultra-low latency and high-efficiency SNN architecture and achieves state-of-the-art performance on CLCXray which is another open-source XRay Threat Detection dataset, hence making improvements in the field of using spiking for object detection. We also analyze the performance of a Spiking YOLO network by converting our QCFS network into a Spiking Network.

翻译：本文实现并分析了多种网络结构，以评估它们在边缘设备上的适用性，从而解决从X射线安检成像数据中检测威胁目标的问题。目前已有大量研究致力于将深度学习技术自动应用于该问题。我们在自研的Tiny YOLOv7模型中采用了一种替代激活函数，该函数与脉冲激活函数相比，经计算其预期转换误差为零。这种基于QCFS的Tiny YOLO版本复现了超低延迟、高效率脉冲神经网络架构的激活特性，并在另一个开源X射线威胁检测数据集CLCXray上取得了当前最优性能，从而推动了脉冲神经网络在目标检测领域的发展。此外，我们还通过将QCFS网络转换为脉冲网络，分析了脉冲YOLO网络的性能表现。