多尺度特征融合的安检图像危险品检测

Dangerous Goods Detection Based on Multi-Scale Feature Fusion in Security Images

现有的目标检测算法检测X光安检图像中较小尺寸的危险品精度较低,为此提出一种多尺度特征融合检测网络,即MFFNet(Multi-scale Feature Fusion Network),其以SSD检测模型为基础并采用更深的特征提取网络,即ResNet-101。通过跳跃连接的方式将网络的高层语义丰富特征与低层边缘细节特征进行融合,为小尺度危险品的检测添加上下文信息,可以有效提升对小尺度目标的识别与定位精度。将融合得到的新特征层与SSD扩展卷积层一起送入检测。实验结果表明,MFFNet能够使X光安检图像中的危险品特别是较小尺寸的危险品,检测精度得到较大的提升,同时能够保持相对较快的检测速度,满足现代化安检的要求。

Existing target detection algorithms have low accuracy in detecting smaller-sized dangerous goods in X-ray security inspection images.Therefore,a multi-scale feature fusion detection network called MFFNet(Multi-scale Feature Fusion Network)is proposed,which is based on the SSD detection model and uses a deeper feature extraction network,namely ResNet-101.The high-level semantic rich features of the network are merged with the low-level edge detailed features through the jump connection method,and contextual information is added for the detection of small-scale dangerous goods,which can effectively improve the identification and positioning accuracy of small scale targets.The new feature layer obtained by fusion and the SSD extended convolution layer are sent into detection together.Experimental results show that MFFNet can greatly improve the detection accuracy of dangerous goods in X-ray security inspection images,especially those of smaller sizes,while maintaining a relatively fast detection speed to meet the requirements of modern security inspection.