Research on the Identification Method of Dangerous Goods in Security Inspection Images Based on Deep Learning

This paper explored the application of deep learning target detection methods in the field of X-ray security screening. Faster R-CNN is a fully supervised deep learning method that uses only abnormal images containing dangerous goods as the training set, thus making it difficult to learn the features of normal images. It results in its high false detection rate when detecting normal images. In view of the above problems, combined with the characteristics of most of the X-ray security images are normal images, the author proposed a pre-classified head X-ray security image recognition method to reduce the false detection rate, while improving the performance and efficiency of dangerous goods detection, and more suitable for real X-ray security application scenarios.