关键词: 可疑物检测, YOLO算法, Dense-YOLO算法, K-means算法, 多尺度训练

Abstract:

In crowded places, security check is an important means to ensure public safety. Artificial problem for security in the event of emergencies or peak passenger flow, the efficiency and accuracy of screening is susceptible and there are security risks, an improved target detection algorithm Dense-YOLO is proposed based on YOLO algorithm. Characterized by drawing a dense network convergence to improve network structure, target block dimension is clustered by using a modified K-means algorithm in an abnormal image data set. Calculation efficiency is lifted by convolution and batch normalization integration, and increasing model robustness is increased to different sizes by multi-scale training. Experimental results show that the improved Dense-YOLO algorithm improves the detection of small targets, and detects the suspicious objects in the security inspection. The mAP reaches 91.68% and the detection speed increases to 59?f/s. The improved algorithm effectively improves the efficiency and accuracy of security checking, which eliminates security risks to a certain extent.

Key words: suspicious object detection, YOLO algorithm, Dense-YOLO algorithm, K-means algorithm, multi-scale training