引入通道注意力和残差学习的目标检测器

现有目标检测器特征金字塔无法充分利用不同尺度特征图的特征信息,不适用于低分辨率图像的目标和小目标的检测.针对此问题,文中提出引入通道注意力机制和残差学习块的目标检测器.首先引入通道全局注意力机制,通过网络学习特征图中不同通道特征的权重,增强有效的全局特征信息.然后采用轻量级的残差块,突出特征的微小变化,提高低分辨率图像中小目标的检测性能.最后在用于预测的浅层特征图中融合深层特征,提高小目标的检测精度.在标准测试数据集上的实验表明,文中目标检测器适用于低分辨率图像,对小目标的检测效果较优.

Target Detector with Channel Attention and Residual Learning

The feature information of feature maps of different scales cannot be fully utilized by the existing feature pyramid of target detectors, and these detectors are not suitable for the detection of low-resolution image targets and small targets. To solve this problem, a target detector with channel attention mechanism and residual learning block is proposed. Firstly, the channel global attention mechanism is introduced to learn the weights of different channel features in the feature map through the network and thus the global feature information is enhanced effectively. Then, lightweight residual blocks are exploited to highlight small changes of features and improve the detection performance for small targets in low-resolution images. In addition, deep features are merged into the shallow feature maps for prediction to improve the detection accuracy of small targets. The experimental results on standard test datasets show that the proposed target detector is suitable for low-resolution images and obtains a better detection result for small targets.