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

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.