智能检测子弹算法模型的训练数据

本数据知识产权数据集包含多角度、多场景下的子弹的X光安检图像，通过对图像的标注、抠图、增强、融合等处理，可作为优质样本训练生成智能检测子弹的算法模型，实现对藏匿在其他物品中或伪装成其他物品等情境下的子弹精准识别。生成的模型可应用在各类安检场景中。
1、数据来源：应用X射线光源多角度、多场景下透射子弹，采集并建立其原始的X光数据图例库。 2、数据深度处理：对采集到的原始X光图像预标注坐标位置和品项类别，并对子弹的图像进行抠图处理。将抠出的图像与多场景图像分别进行几何变换、像素变换等增广处理。 3、检测模型生成算法规则：将处理后的子弹的X光安检图像和场景X光图像通过密度统计（像素值代表实物密度值）依据区域匹配原则进行融合，融合区域掩模作为数据标签与融合后的图像作为深度学习样本数据。还可通过调整抠图区域在场景图像区域的位置，获得不同的平均密度差值，训练生成可精准定位、精准识别子弹的智能检测模型。区域匹配原则按照Mask*(α*ρ抠图图像+β*ρ场景图像)，融合后的图像处理公式按照Mask*(α*ρ抠图图像+β*ρ抠图图像)+（1-Mask）*ρ场景图像。（所述公式中：Mask为图像掩膜，图像目标区域值为1，目标区域外值为1，ρ为密度值，α、β指系数）检测模型可对多场景下的子弹精准识别，同时将目标物的位置及所在X光图像信息记录标出。进一步的还可根据目标物位置信息推算目标物尺寸信息。

This intellectual property dataset contains X-ray security inspection images of bullets captured from multiple angles and scenarios. Through processing such as image annotation, matting, augmentation and fusion, it can serve as high-quality samples to train intelligent bullet detection algorithm models, enabling accurate identification of bullets hidden in or disguised as other items. The trained models can be applied to various security inspection scenarios.
1. Data Source: Perform X-ray transmission on bullets under multiple angles and scenarios using X-ray sources, collect and establish an original X-ray image database.
2. Data Deep Processing: Pre-annotate the coordinate positions and item categories for the collected original X-ray images, and conduct image matting on the bullet images. Subsequently, perform augmentation processing including geometric transformation and pixel transformation on the extracted bullet images and multi-scenario images respectively.
3. Detection Model Generation Algorithm Rules: Fuse the processed X-ray security inspection images of bullets and scenario X-ray images according to the regional matching principle through density statistics (pixel values represent physical density values). The fusion region mask is used as the data label, and the fused images are used as deep learning sample data. Additionally, by adjusting the position of the matting region within the scenario image region, different average density differences can be obtained to train an intelligent detection model that can accurately locate and identify bullets. The regional matching principle follows the formula: Mask * (α * ρ_matting + β * ρ_matting), and the image processing formula for the fused image follows: Mask * (α * ρ_matting + β * ρ_matting) + (1 - Mask) * ρ_scenario. (In the above formulas: Mask is the image mask, the value of the target area in the image is 1, and the value outside the target area is 1; ρ represents the density value; α and β are coefficients.) The detection model can accurately identify bullets in multiple scenarios, while recording and marking the position of the target object and the information of the X-ray image where it is located. Furthermore, the size information of the target object can be inferred based on its position information.

搜集汇总

数据集介绍

特点

该数据集包含813条X光安检图像，用于训练智能检测子弹的算法模型，适用于多场景下的子弹精准识别，每半年更新一次。