A Contraband Detection Method for X-ray Security Images Based on Improved YOLOv8
The efficiency of manual security checks is low and prone to errors.Implementing automatic security checks based on artificial intelligence is the development trend of security checks.The YOLOv8 object detection model has been improved to address the issues of low detection accuracy and high missed detection rate for a small number of categories in X-ray prohibited item detection.On the basis of YOLOv8n,the network structure was modified,attention mechanism was introduced,and a YOLOv8n-ECA object detec-tion model with Efficient Channel Attention(ECA)was proposed to better extract the features of prohibited items in X-ray images.At the same time,a series of data augmentation methods such as image rotation were used to expand the sample size for a small number of category samples.Experiments were conducted on a self-building X-ray security inspection image dataset,and the results showed that the improved algorithm enhanced detection accuracy by 6％compared to the original YOLOv8n model,increased detection speed by 15.7％compared to the original YOLOv8n model,and reduced the missed detection rate of a small number of categories.
万方数据
维普
