A Study on Tiny YOLO for Resource Constrained Xray Threat Detection
- URL: http://arxiv.org/abs/2309.15601v2
- Date: Mon, 6 Nov 2023 20:31:46 GMT
- Title: A Study on Tiny YOLO for Resource Constrained Xray Threat Detection
- Authors: Raghav Ambati, Ayon Borthakur
- Abstract summary: This paper implements and analyzes multiple networks with the goal of understanding their suitability for edge device applications such as X-ray threat detection.
We use the state-of-the-art YOLO object detection model to solve this task of detecting threats in security baggage screening images.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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