In order to solve the problems of poor detection performance and slow detection speed of the active vision security inspection method, a Heuristically Accelerated State Backtracking Q-Learning (HASB-QL) algorithm based on Q-Learning (QL) algorithm was proposed to estimate the next-best-view. The cost function and heuristic function were introduced by the proposed algorithm to improve the learning efficiency and speed up the convergence of QL. Firstly, the single view detection of X-ray images obtained by security scanner was performed. Secondly, the pose was estimated and the best rotation angle was obtained by comparing the selection strategy of repeated action in the state backtracking process, and then the single view detection was performed again until the threat object was detected. Moreover, the geometric constraint was established to eliminate the false alarms when the number of views was more than one in detection process. The X-ray images of handguns and razor blades in GDXray data set were used for the experiment. The experimental results show that, compared with active vision algorithm based on QL, the weighted average value of FF ₁ between the precision and recall of detecting the handguns by the improved active vision algorithm is increased by 9.60% and the detection speed is increased by 12.45%, while the F ₁ of detecting razor blades is increased by 2.51% and the detection speed is increased by 17.39%. The proposed algorithm can improve the performance and speed of threat object detection.