基于全局注意力的多级特征融合目标检测算法

针对小目标物体检测精度差的问题,同时不以牺牲速度为代价,本文提出了一种基于全局注意力的多级特征融合目标检测算法。算法首先由卷积神经网络生成多尺度的特征图,然后采用多级特征融合的方法,将浅层和深层特征图的语义信息相结合,提高特征图的表达能力,接着引入全局注意力模块,对特征图上下文信息进行建模,并捕获通道之间的依赖关系来选择性地增强重要的通道特征。此外,在多任务损失函数的基础上增加一项额外的惩罚项来平衡正负样本。最后经过分类回归、迭代训练和过滤重复边框得到最终检测模型。对所提算法在PASCAL VOC数据集上进行了训练和测试,结果表明该算法能有效地提升小目标物体检测效果,并较好地平衡了检测精度与速度之间的关系。

Multi-level Feature Fusion Object Detection Algorithm Based on Global Attention

Aiming at the problem of poor detection accuracy of small target objects without sacrificing speed, this paper proposes a multi-level feature fusion object detection algorithm based on global attention. The algorithm first generated a multi-scale feature map from a convolutional neural network, and then used a multi-level feature fusion method to combine the semantic information of the shallow and deep feature maps to improve the expression ability of the feature map. Then, the global attention module was introduced to model the feature map context information and captured the dependencies between channels to selectively enhance important channel features. In addition, an additional penalty term was added to the multi-task loss function to balance the positive and negative samples. Finally, the final detection model was obtained through classification regression, iterative training, and filtering repeated borders. The proposed algorithm was trained and tested on the PASCAL VOC dataset, and the results show that the algorithm can effectively improve the detection effect of small targets and balance the relationship between detection accuracy and speed.