基于边缘填充的单兵迷彩伪装小目标检测  

Single soldier camouflage small target detection based on boundary-filling

作　　者：池盼盼 梅琛楠 王焰 肖红 钟跃崎[1,3] CHI Panpan;MEI Chennan;WANG Yan;XIAO Hong;ZHONG Yueqi(College of Textiles,Donghua University,Shanghai 201620,China;Systems Engineering Research Institute,Academy of Military Sciences,Beijing 100010,China;Key Laboratory of Textiles Science&Technology,Ministry of Education,Donghua University,Shanghai 201620,China)

机构地区：[1]东华大学纺织学院,上海201620 [2]军事科学院系统工程研究院,北京100010 [3]东华大学纺织面料技术教育部重点实验室,上海201620

出　　处：《纺织学报》2024年第1期112-119,共8页Journal of Textile Research

基　　金：上海市自然科学基金项目(21ZR1403000);国家自然科学基金项目(61572124)。

摘　　要：针对迷彩单兵识别存在伪装对象与背景高度相似融合、目标尺寸小等问题,提出了基于边缘填充的单兵迷彩伪装小目标检测模型BFNet(boundary-filled network)。该网络以SCNet(sparse complex-valued neural network)作为骨干网络,在网络的边缘引导阶段,利用边缘先验信息以及边缘的周围环境来挖掘目标信息。在上下文聚合阶段,利用上一级的预测值,使网络学习预测背景与前景的相互关系。实验结果表明:与最先进的BGNet相比,BFNet平均精度提升了0.74%,交并比识别率提升了1.35%,同时自适应E度量、加权F度量以及结构相似度与加权自适应F度量均得到了提高,其中,自适应E度量提升了0.85%,加权F度量提升了0.71%,证明所提出的BFNet能在更大程度上识别出单兵迷彩伪装小目标,且识别精度也得到提升。Objective In the automatic detection of siagle soldier camouflage,it is necessary to detect the targetsat a long distance.In this scenario,the small size of the camouflaged target and the intensification of background fusion substantially increase the difficulty of detection.Therefore,a deep learning approach to tackle this challenge is proposed based on the deep learning network architecture and module structure.Method The original dataset was extended using data augmentation and the network architecture was designed based on the BGNet model.SCNet was used for feature extraction of images,and EAM(edge-aware module)was used for detecting target edges.EFM(edge-guidance feature module)made use of the output of EAM to guide the network to locate and identify targets,NCD(neighbor con-connection decoder)was used for fusing the features from EFM output,and the CAM(context aggregation module)was employed to aggregate multi-level features to obtain the final output.Results The quantitative resultsof the proposed model and the other models showed that PFNet performed poorly in this small target detection,and SINet-V2 and C2FNet had higher recognition rates but with lower recognition accuracy,indicating poor detection accuracy although they intersect with the true values.On the other hand,the BGNet model had lower recognition rates but with higher accuracy and structural similarity.The BFNet proposed in this paper was improved based on the BGNet,and after the improvement,the recognition rate was increased.At the same time,other indices measuring detection accuracy and object similarity were also improved.The proposed BFNet was found to be able to take both recognition rate and accuracy rate into account,and identify targetsmore accurately and comprehensively.The quantitative evaluation of the ablation experimentswas carried out,and it showed that the modified EFM improved the recognition rate I by 1.35%,indicating that more targetsare able to be recognized after the improvement.The modified CAM improved the recognition rat

关 键 词：单兵迷彩伪装自动检测 伪装物体识别 深度学习 小目标检测 伪装物体分割 

分 类 号：TS131.9[轻工技术与工程—纺织材料与纺织品设计]