基于递进式特征增强聚合的伪装目标检测

伪装目标检测（COD）旨在检测隐藏在复杂环境中的目标。现有COD算法在结合多层次特征时，忽略了特征的表达和融合方式对检测性能的影响。为此，提出一种基于递进式特征增强聚合的COD算法。首先，通过主干网络提取多级特征；然后，为了提高特征的表达能力，使用由特征增强模块（FEM）构成的增强网络对多层次特征进行增强；最后，在聚合网络中设计邻近聚合模块（AAM）实现相邻特征之间的信息融合，以突显伪装目标区域的特征，并提出新的递进式聚合策略（PAS）通过渐进的方式聚合邻近特征，从而在实现多层特征有效融合的同时抑制噪声。在3个公开数据集上的实验表明，所提算法相较于12种最先进的算法在4个客观评价指标上均取得最优表现，尤其是在COD10K数据集上所提算法的加权的F测评法和平均绝对误差（MAE）分别达到了0.809和0.037。由此可见，所提算法在COD任务上拥有较优的性能。

Camouflaged object detection based on progressive feature enhancement aggregation

Camouflaged Object Detection （COD） aims to detect objects hidden in complex environments. The existing COD algorithms ignore the influence of feature expression and fusion methods on detection performance when combining multi-level features. Therefore， a COD algorithm based on progressive feature enhancement aggregation was proposed. Firstly， multi-level features were extracted through the backbone network. Then， in order to improve the expression ability of features， an enhancement network composed of Feature Enhancement Module （FEM） was used to enhance the multi-level features. Finally， Adjacency Aggregation Module （AAM） was designed in the aggregation network to achieve information fusion between adjacent features to highlight the features of the camouflaged object area， and a new Progressive Aggregation Strategy （PAS） was proposed to aggregate adjacent features in a progressive way to achieve effective multi-level feature fusion while suppressing noise. Experimental results on 3 public datasets show that the proposed algorithm achieves the best performance on 4 objective evaluation indexes compared with 12 state-of-the-art algorithms， especially on COD10K dataset， the weighted F-measure and the Mean Absolute Error （MAE） of the proposed algorithm reach 0.809 and 0.037 respectively. It can be seen that the proposed algorithm achieves better performance on COD tasks.