基于特征增强的模糊红外刑侦目标提取算法研究

本研究旨在解决红外图像刑侦场景中目标边缘模糊和轮廓不完整等问题。提出了一种基于特征增强的模糊刑侦目标提取方法。首先，设计模糊刑侦目标边缘提取网络模型(Blurry Criminal Investigation Target Edge Extraction Network Combined with Spatial Channel Attention, BCES-Net)，并利用空间通道关注模块(Spatial and Channel Attention Module, STCAM)获取具有强语义信息的特征图像。接着通过建模提取来获取包含语义类别信息的边缘特征和模糊刑侦目标特征。在训练过程中，基于特定损失函数和多种特征融合技术，通过反复监督学习和训练校正，提高了边缘和模糊刑侦目标分割性能。在手部热痕迹数据集上，与DeeplabV3+、U-Net、HRNet、PSPNet等模型相比，BCES-Net模型在均交并比(mean Intersection over Union, mIoU)、平均精度均值(mean Average Precision, mAP)、准确率等评价指标上显著优越，mIoU达到88.3%，mAP达到94.35%，准确率达到95.5%。本研究创新性地提高了模糊红外刑侦目标提取的准确度，为实际应用提供了技术支持。

Research on Fuzzy Infrared Criminal Investigation Target Extraction Algorithm Based on Feature Enhancement

The aim of this study is to solve the problems of blurred edges and incomplete contours in infrared image criminal investigation scenes. A method of extracting fuzzy criminal investigation targets based on feature enhancement is presented in this paper. Firstly, the BCES-Net network model is designed and the feature images with strong semantic information are obtained by using STCAM. Then the edge features and fuzzy criminal investigation target features containing semantic category information are obtained by modeling extraction. In the training process, based on specific loss functions and multiple feature fusion techniques, the segmentation performance of edge and fuzzy criminal investigation targets is improved through repeated supervised learning and training correction. In the hand heat trace data set, compared with DeeplabV3+, U-Net, HRNet, PSPNet and other models, BCES-Net is significantly superior in mIoU, mAP, accuracy and other evaluation indexes. mIoU reaches 88.3%, mAP reaches 94.35%, and the accuracy reaches 95.5%. This research innovatively improves the extraction accuracy of fuzzy infrared criminal detection targets and provides technical support for practical application.