摘要
鞋印图像识别是计算机视觉在公安一线工作中的一项重要应用。当前公安侦查工作中鞋印图像无法进行精准识别的问题制约了工作效率与质量的提高,归纳起来主要是囿于鞋印现场提取的复杂情况、鞋印花纹图样的复杂特征以及鞋印图像的残缺不全。针对残缺鞋印,为了进一步提高残缺鞋印检索结果,设计了一种融合特征筛选的双塔网络鞋印检索算法。一方面,在网络中引入分区策略,将鞋印图像分为足掌区和足跟区用两个特征网络分别提取图像特征进行融合;另一方面,选择融合ResNet网络和Transformer网络的新型卷积神经网络convNeXt网络作为骨干网络,加入注意力机制模块,提取最后一层卷积特征后用不同的特征筛选方法去除鞋印图像中的无关特征,最后拼接展开成为特征描述符进行相似度计算。在训练阶段,优化学习策略,将其作为完整的图像分类网络进行训练。实验结果表明,本文选取的网络模型优于其他卷积神经网络,在CSS-200和FID-300两个鞋印数据集上取得了较高的准确率。
Shoe printing image identifies is an important application in computer vision in the work of public security.The current problems that cannot be accurately recognized in the public security investigation work restrict the improvement of work efficiency and quality due to the complexity of the shoe printing field extraction,the complex characteristics of the shoe print pattern and the incompleteness of the shoe printing images.In order to further improve the results of the printing of disabled shoes,a double-tower network shoe print retrieval algorithm based on feature screening was designed.On the one hand,a partitioning strategy was introduced into the network to divide the shoe printing image into the sole region and the heel region,and two feature networks were used to extract the image features respectively for fusion.On the other hand,convNeXt network,a new convolution neural network that integrates ResNet network and Transformer network,was selected as the backbone network to add attention mechanism module.After extracting the last layer of convolution features,different feature screening methods were used to remove the irrelevant features in the shoeprint image.Finally,the feature descriptor was spliced and expanded to calculate the similarity.In the training phase,the learning strategy was optimized,and it is trained as a complete image classification network.The experimental results show that the network model selected in this paper is better than other convolutional neural networks,and the CSS-200 and FID-300 two shoe print data sets have achieved high accuracy.
作者
韩雨彤
郭威
唐云祁
HAN Yu-tong;GUO Wei;TANG Yun-qi(Chinese People's Public Security University Investigation College,Beijing 100032,China)
出处
《科学技术与工程》
北大核心
2023年第22期9576-9584,共9页
Science Technology and Engineering
基金
公安部技术研究计划(2020JSYJC21)
中央高校基本科研业务费项目(2022SJKJS07)。
