Abstract: The detection of such dangerous targets as guns has always been one of the important research subjects in the field of security. Manual inspection of guns and other dangerous objects through surveillance video is inefficient, the accuracy of which can be easily affected by unfavorable conditions of inspectors due to continuous working. Therefore, a real-time gun detection method using the pruning method to compress the YOLOV3 model was proposed. In order to improve model accuracy, the K-means ++ algorithm was adopted to cluster Anchor size of data set. Then the pruning method of “channel + layer” was employed to compress the trained model. Finally, by means of retraining, the accuracy was restored to that before compression. The experimental results show that while still maintaining high accuracy, this method can reduces not only the model’s occupation of memory resources, but also the computing load, greatly improving the inference speed of the model. Compared with the YOLOV3 method, this method reduces the model parameters by up to 1/52 on the jetson nano platform and increases the reasoning speed by 6 times, with the accuracy almost unchanged, thus meeting the requirements of real-time and high-precision detection of such dangerous objects as guns. 

Key words: gun detection, YOLOV3, model compression, K-means ++, real-time detection