## 1 Introduction
[6] used the edge detection operator to extract the golden region of the image. Their work indicated that image feature extraction by the edge detection operator is more sensitive to the texture region in the image. However, the features extracted from the image region with weak texture cannot form closure, which leads to errors in the subsequent contour detection, thus affecting the effect of target extraction. [PERSON] and [PERSON] [7] used the binarization method of the LoG edge detection operator; when the image color of cardboard boxes, backpacks and other items is similar to the background color, the effect is better. This is because the binarization method based on the edge detection operator is more sensitive to the gradient change in the image, and it can accurately detect the intersection of the edge and background. But for large areas of the image with the same color and less texture, the edge detection operator cannot offer a better detection effect. Threshold method is a classical target extraction algorithm which has been widely used in many fields [8; 9; 10; 11]. The method optimizes a threshold through some algorithm, the final threshold value as the dividing line, greater than the threshold value for the change class and less than the threshold value for the unchanged class [12]. The threshold method deals with determination of the threshold value. The maximum expected value algorithm is the EM algorithm. Based on the segmentation method of automatic selection of multiple thresholds, [PERSON] [13] used the watershed algorithm based on markers to extract the image histogram and obtain multiple thresholds. On the basis of threshold segmentation, the watershed algorithm is used to segment the image, so as to extract the region of interest in the X-ray image. [PERSON] [14] applied the maximum expected value algorithm of Gaussian model to the analysis of difference graph. The difference graph was modeled. The varying pixels and invariant pixels obey the Gaussian distribution, respectively, and the threshold value is obtained through multiple iterations. The parameters of the model are obtained through multiple iterations of EM algorithm. [PERSON] et al. [PERSON] et al. (2015) applied the KI (Kittler-Illingworth) threshold method to the difference graph analysis algorithm. The KI threshold method is based on the Bayes theory of minimum error. Later, the researchers improved the model to produce a generalized KI threshold algorithm [PERSON] et al. (2016). The threshold method is simple and fast, but it cannot use spatial information effectively. This paper focuses on the accurate extraction of the luggage package target. An ES-MBD (Edge Sensitive Multi-channel Background Difference Algorithm) method based on edge-sensitive multi-channel background difference is proposed to achieve more accurate image binarization. In addition, the Suzuki contour detection algorithm is adopted to detect the contour of the binary image obtained by the ES-MBD method to subsequently match the area of the package box and then make a judgment. The detection result is output as the key target of the X-ray image. The main contributions of this paper are as follows:  
1. In this paper, the ES-MBD method is proposed. It solves the problems of low detection efficiency and high GPU overhead due to the unfixed shape of luggage packages, stacking, and occlusion in the video. The ES-MBD method is able to improve the detection efficiency and extract a more complete image of luggage packages. 2. The ES-MBD method combines the background difference binarization and edge detection operator binarization, which solves the problem of the binarization method of background difference being insensitive to texture features, while the binarization method based on edge detection operator is insensitive to smooth regions. Through experimental comparison, the precision rate of the ES-MBD binarization method reaches 97.3% and the recall rate reaches 96.5%, and the ES-MBD method has obvious advantages in the luggage package target extraction of X-ray images.