刑事案件现场图作为刑事案件现勘记录的重要组成部分,在法庭科学领域中发挥着重要作用,然而在公安实战中,现场图绘制不规范的情况仍然时有发生。基于此,提出一种基于卷积神经网络的现场图自动分类方法,实现对全国公安机关现场勘验信息系统(简称为现勘系统)中现场图的自动分类核查。首先,利用现勘系统中现场图构建刑事案件现场图数据集,包括64098幅现场图和作为负类的27162张现场照片;然后,在AlexNet的基础上引入Inception结构,提出适用于现场图分类问题的卷积神经网络结构XCTNet;最后,多维度展现XCTNet的性能,并提取出分类错误的图像。实验结果表明:XCTNet在参数量仅为AlexNet的10%的条件下,在测试集上的准确度达到了98.65%,相比较AlexNet提升了3.78个百分点,但对自绘方位示意图的识别精度仍需要进一步提高。

A crime scene sketch plays an important role in forensic science as an important part of investigation records. However, in real life, unqualified sketches are developed in many cases. Therefore, this study proposes an automated method to classify crime scene sketches based on a convolutional neural network. First, 64098 crime scene sketches and 27162 photos used as negative samples are collected from the national criminal scene investigation information system (crime scene survey system for short), and are manually labeled to build a crime scene sketch dataset. Then, a new convolutional neural network called XCTNet is designed by introducing “Inception” into AlexNet. Finally, the performance of XCTNet is measured with respect to many aspects, and the images misclassified by XCTNet are extracted. The results denote that XCTNet achieves an accuracy of 98.65% on the test set, which is 3.78 percentage points higher than that of AlexNet; meanwhile, it only uses one tenth of the parameters of AlexNet. However, the recognition accuracy of the proposed method for self-drawn location sketches should be improved.