计算机工程与应用2025，Vol.61Issue(10)：279-287,9.DOI:10.3778/j.issn.1002-8331.2403-0003
融合二阶池化注意力的类边界均衡小样本红外目标检测
Few-Shot Infrared Object Detection with Class Margin Equilibrium Based on Second-Order Pooling Attention
摘要
Abstract
To address the issue of decreased detection performance in infrared object detection due to the inability of existing few-shot object detection models to fully exploit support information in the presence of complex background interference,a novel few-shot object detection algorithm is proposed that integrates second-order pooling attention with class margin equilibrium.The algorithm of class margin equilibrium achieves boundary balance between new classes by adversarial minimum-maximum regularization on class margin,addressing the performance degradation in few-shot object detection tasks caused by the confusion of feature prototype distributions between base classes and novel classes.However,due to complex background interference,directly applying the algorithm of class margin equilibrium to infrared object detection cannot fully utilize the effective information from the support set images.The second-order pooling atten-tion mechanism is proposed to suppress background interference,enhance the learning of effective information from support set images,and thereby strengthen the function of utilizing support information to adjust query information.This mechanism calculates the covariance between different channels of the input feature map along the channel dimension to capture the statistical dependencies among channels,and thereby capture the high-order feature information of important channels.At the same time,the standard deviation of each channel of the input feature map is computed along the channel dimension.The covariance between two channels is then divided by the product of their respective standard deviations to reduce the impact of noise on the covariance estimation and enhance the accuracy of covariance calculation.Incorporating the second-order pooling attention mechanism into the weighting module of class margin equilibrium guides the detec-tion algorithm to focus feature learning on object and its surroundings,suppressing interference from complex back-grounds.The experimental results indicate that compared to classical algorithms,the proposed few-shot object detection algorithm achieves the best performance in the 10-shot task,with an mAP of 56.4%on novel classes in homemade infrared object dataset.关键词
红外目标检测/小样本/类边界均衡/二阶池化注意力/协方差Key words
infrared object detection/few-shot/class margin equilibrium/second-order pooling attention/covariance分类
信息技术与安全科学引用本文复制引用
司起峰,刘刚,徐红鹏,陈会祥..融合二阶池化注意力的类边界均衡小样本红外目标检测[J].计算机工程与应用,2025,61(10):279-287,9.基金项目
国家留学基金(留金项[2022]20号) （留金项[2022]20号）
河南省高等学校重点科研项目(21A520012). （21A520012）
