基于注意力机制的多尺度仪表检测

为实现复杂场景下多尺度仪表检测，提出了一种基于注意力机制的视频多尺度仪表检测算法。首先，利用基于空间注意力机制的特征提取网络，建模特征的长距离依赖，增强特征的表达能力；其次，提出了一种自适应特征选择模块（Adaptive Feature Selection Module, AFSM），对不同阶段的特征图进行权重调整，增强网络对多尺度目标的检测能力。在自建的仪表数据集上进行了实验。实验结果表明，相比较原来的Faster RCNN方法，所提出方法的检测精度提高了7.6%；与对比方法相比，检测精度也能达到95.4%。在对实际仪表监测视频的测试中，检测结果以及速度能够满足实际需要。所提方法通过改进特征提取网络和特征选择操作，增强了特征表达能力，有效降低了虚警，提升了网络对多尺度目标的检测性能。

Multi-scale instrument detection based on attention mechanism

In order to realize multi-scale instrument detection in complex scenes, a video multi-scale instrument detection algorithm based on attention mechanism was proposed. Firstly, the feature extraction network based on spatial attention mechanism was used to model the long distance dependence of features and enhance the expression ability of features. Secondly, an Adaptive Feature Selection Module (AFSM) is proposed to adjust the weight of feature maps in different stages to enhance the capability of multi-scale target detection. The experiment was carried out on a self-built instrument data set. Experimental results show that, compared with the original Faster RCNN method, the detection accuracy of the proposed method is improved by 7.6%. Compared with the comparison method, the detection accuracy can also reach 95.4%. In the actual instrument monitoring video test, the detection results and speed can meet the actual needs. By improving the feature extraction network and feature selection operation, the proposed method enhances the feature expression ability, effectively reduces the false alarm, and improves the multi-scale target detection performance of the network.