注意力与跨尺度融合的SSD目标检测算法

SSD Object Detection Algorithm with Attention and Cross-Scale Fusion

为了进一步提升SSD算法的性能,解决SSD算法在进行多尺度预测时特征图信息不平衡和小目标识别难的问题,设计了即插即用的模块,充分融合不同尺度特征图包含的信息并建模特征图内的重要性关系,来增强特征图的表示能力。首先,设计了一种新颖的特征融合方法来解决跨尺度特征融合存在的信息差异问题。其次,根据池化金字塔的思想设计了一种深度特征提取模块来提取不同感受野的信息,从而提高模型对不同尺寸目标的检测能力。最后,为了进一步优化特征图,突出特征图对当前任务有效的信息,并建立全局像素点之间的长距离关系和各通道之间的重要性关系,提出了一种轻量级的注意力模块。通过上述机制,修改了SSD模型的架构,有效地提升了SSD算法的检测精度和鲁棒性。在PASCALVOC数据集上设计了丰富的实验,验证了所提方法的有效性。在PASCALVOC2007测试集上该方法比SSD算法提高了2.9个百分点的平均精确度(mAP),同时还保留了实时检测的能力。

In order to further improve the performance of the SSD(single shot multibox detector)algorithm,and solve the problems of unbalanced feature map information and difficulty in small target recognition during multiscale prediction of the SSD algorithm,in this paper,plug-and-play modules are designed to fully integrate the information contained in feature maps of different scales and model the relationships within feature maps to enhance the representation ability of feature maps.Firstly,a novel feature fusion method is designed to solve the problem of information disparity in cross-scale feature fusion.Secondly,according to the idea of pooling pyramid,a depth feature extraction module is designed to extract the information of different receptive fields,so as to improve the detection ability of the model to object of different sizes.Finally,in order to further optimize the feature map,highlight the effective information of the feature map for the current task,and establish the global long-distance relationship between pixels and the importance relationship between each channel,a lightweight attention module is proposed.Through the above mechanism,the structure of SSD model is modified in this paper,which effectively improves the detection accuracy and robustness of SSD algorithm.Extensive experiments have been conducted on PASCAL VOC datasets to verify the efficiency of the proposed method.On PASCAL VOC2007 test datasets,the proposed method improves 2.9 percentage points mean average precision(mAP)over SSD algorithm,while maintaining the ability of real-time detection.