轻量级红外刑侦图像目标识别算法

Lightweight Target Recognition Algorithm for InfraredCriminal Investigation Images

红外刑侦图像目标识别对刑事侦查具有重要意义,但刑事案件的侦破对时间和置信度要求较高。设计一种保持优异识别精度且具备较快识别速度的轻量级红外刑侦图像目标识别算法,具有十分重要的研究价值。因此借鉴生物免疫的优良特性,设计了免疫原性深度神经网络算法。该算法通过构建先天性免疫网络和适应性免疫网络来提取图像特征,然后设置免疫原性网络增强算法在处理图像特征映射时对不同通道之间优先级的调整能力,从而提高算法的精度和速度。实验结果表明,本文算法有效实现了红外刑侦图像的快速精准识别。与VGG16、VGG19、Resnet34、Resnet50、MobilenetV2等模型相比,本文算法不仅取得了99.4%的最高测试准确率,而且还具备最快的识别速度。

Infrared image target recognition is of great significance to criminal investigation, but the resolution of criminal cases demands high requirements in terms of time and confidence coefficient. It is of great research value to design a lightweight target recognition algorithm of infrared criminal detection image to maintain excellent recognition accuracy and high recognition speed at the same time. Therefore, the excellent characteristic of biological immunity is drawn on and the immunogenic deep neural network algorithm is designed. The algorithm constructs innate immune network and adaptive immune network to extract image features. Then an immunogenic network enhancement algorithm is implemented to adjust the priority between different channels when processing image feature mapping, so as to improve the accuracy and speed of the algorithm. Experiments show that the proposed algorithm can effectively realize the fast and accurate recognition of infrared criminal detection images. When compared with VGG16, VGG19, Resnet34, Resnet50, MobilenetV2 and other models, the proposed algorithm not only achieves the highest test accuracy of 99.4%, but also has the fastest recognition speed.