Deep Learning-Based X-Ray Baggage Hazardous Object Detection – An FPGA Implementation

Abstract

This work proposes deep learning neural network-based X-ray image classification. The X-ray baggage scanning machinery plays an essential role in the safeguard of customs, airports, and other systematically very important landmarks and infrastructures. The technology at present of baggage scanning machines is designed on X-ray attenuation. The detection of threatful objects is built on how different objects attenuate the X-ray beams going through them. In this paper, the deep convolutional neural network of YOLO is utilized in classifying baggage images. Real-time performance of the baggage image classification is an essential one for security scanning. There are many computationally intensive operations in the You Only Look Once (YOLO) architecture. The computational intensive operations are implemented in the Field Programmable Gate Array (FPGA) platform to optimize process delays. The critical issues involved in those implementations include data representation, inner products computation and implementation of activation function and resolving these issues will also be a significant task. The FPGA implementation results show that with less resource occupancy, the YOLO implementation provides maximum accuracy of 98.9% in classifying X-ray baggage images and identifying hazardous materials. This result proves that the proposed implementation is best suited for practical system deployments for real-time Baggage scanning.