判别相关分析双注意力机制的目标检测算法

针对两阶段目标检测算法中模型存在目标识别率低、部分小目标物漏检等问题，提出了一种基于判别相关分析的双注意力机制的目标检测算法。该算法通过改进Faster R-CNN主干网络，引入判别相关分析技术最大化两个特征集中对应特征的相关关系，同时最大化不同类之间的差异，来保证信息间的交互，有效缓解常规特征融合方式存在的特征提取能力不足问题。同时，结合残差结构构建残差双注意力机制，进行深层次的特征提取，来弥补深度CNN后高分辨率信息弱化问题，采用混合卷积层的设计在扩大感受野的同时又减少了信息损失，最大限度地保证了网络的特征提取性能。采用PASCAL VOC2007、KITTI以及Portrait三类数据集对网络进行训练，并将提出的算法模型与多个经典目标检测算法进行对比。实验结果表明，提出的算法具有较高的检测精度。

Object Detection Based on Dual Attention Mechanism Combined with Discriminant Correlation Analysis

For the problems of low target recognition rate and missing detection of some small targets in the model of two-stage target detection algorithm, a target detection algorithm with dual attention mechanism based on discriminant correlation analysis is proposed in this paper. In order to maximize correlation between the corresponding features in two feature sets and differences between different types of features, the Faster R-CNN backbone network is improved and discriminant correlation analysis technology is introduced. It can ensure the interaction of information and effectively alleviate the problem of insufficient feature extraction ability in the conventional feature fusion method. At the same time, a residual dual attention mechanism combined with the residual structure is constructed, which aims to extract the deep-level feature so as to compensate for the weakening of high-resolution information after deep CNN. Meantime, the mixed convolutional layer is adopted to expand receptive field while reducing information loss to maximize the feature extraction performance of network. PASCAL VOC2007, KITTI and Portrait are adopted to train the network, and the proposed algorithm model is compared with multiple classic target detection algorithms. Experimental results demonstrate that the proposed algorithm has high detection accuracy.