基于改进Transformer的井下人员检测算法

Underground Personnel Detection Based on Improved Transformer

针对现有井下人员检测算法的特定后处理步骤,如锚点生成和非极大抑制,导致训练过程复杂的情况,提出了一种基于改进Transformer的检测方法,旨在提高井下作业人员检测的准确性。算法以Detection Transformer为基础检测框架,将主干网络ResNet替代为轻量级的Swin Transformer网络;同时向输入序列中添加可学习的检测块,并采用一种重新配置的注意力模块;以更好地处理多尺度特征的融合,使用轻量级的无编码颈部结构,减少计算开销。通过采集不同工作场景监控视频并制作数据集进行实验,结果表明,提出的方法在自建井下人员检测数据集上表现出色,平均检测准确度达到97.8%,优于其他井下人员检测网络,此外检测速度达到32帧/s。

In order to solve the problem that the specific post-processing steps of the existing downhole personnel detection algorithms,such as Anchor and Non-Maximum Suppression,lead to the complex training process,a detection method based on improved Transformer was proposed to improve the detection accuracy of underground personnel.In this algorithm,Detection Transformer was used as the basic detection framework,and the lightweight Swin Transformer network was used to replace the backbone network ResNet.At the same time,a learnable detection block was added to the input sequence,for which a reconfigured attention module was introduced.In order to integrate multi-scale features,a lightweight,non-encoding neck structure was adopted to reduce the computational costs.Surveillance videos of different working scenarios were collected to create datasets for experiments.The results show that the proposed method has achieved excellent performance on the self-built underground personnel detection dataset,with an average detection accuracy of 97.8%,which is better than that of other underground personnel detection networks,and the detection speed reaches 32 frames per second.