全局通道注意力增强的毫米波图像目标检测

Global channel attention boosted active millimeter wave image object detection

针对主动毫米波图像中目标与背景纹理区分度较低导致隐匿目标漏检问题,并根据安检实时性要求,提出一种基于全局通道注意力增强的主动毫米波图像目标检测方法。该方法以YOLOv5s为载体,在坐标注意力位置方向上引入全局通道注意模块,增强对隐匿目标全局通道信息的关注,从而提升在隐匿目标与背景纹理区分度较低时的检测能力;再利用K-means++聚类算法重新生成适合毫米波图像目标检测的锚框。实验结果表明,无论是阵列图像数据集还是线扫图像数据集,该方法增强了对隐匿目标的特征注意,提高了召回率,在满足安检实时性的前提下,提升了检测性能。通过增加少量参数,在阵列图像数据集上,精度、召回率和mAP@.5达到了92.0%、90.93%和95.32%;在线扫图像数据集上,精度、召回率和mAP@.5达到了94.65%、92.67%和97.73%。平均单张图像推理时间在两个数据集上均达到1 ms,满足实时性要求。

Due to the low discrimination between objects and background texture in active millimeter wave images and the need for security in real time,a global channel attention booster-based method for active millimeter wave image object detection is proposed.In order to improve attention to the global channel information of the concealed object and improve detection performance when the concealed object could not be distinguished from the background texture,this method uses YOLOv5s as the carrier and adds global channel attention to the position direction of coordinate attention.And the K-Means++clustering method is used to create the anchor box for identifying concealed objects in millimeter wave images.The results demonstrate that both for array image dataset and line sweep image dataset,the detection model enhances the attention of hidden objects feature and improves the detection performance on the basis of meeting the security real-time performance.