Research on airport baggage detection based on YOLOv8n
With the rise of deep learning and artificial intelligence,as well as the full coverage of the baggage conveyor line by the closed-circuit television monitoring system,researchers have begun to use image recognition technology to identify and track the baggage throughout the whole process,but in the process of production and operation,the problems of low image quality,baggage sticking and stacking and other problems affecting the accuracy of identification and tracking occur from time to time.To address these problems,an attentional feature enhancement method is proposed to suppress the background information and allow the neural network to extract baggage features with better robustness.In order to verify the effectiveness of the method,a dataset is constructed using bag-gage images from Kunming Airport,on which the detection accuracy of the method reaches 70.2％,as well as a very small number of parameters,which provides support for the efficiency and accuracy improvement of baggage processing.
万方数据
维普
