Research on indoor anti-theft system based on YOLOv5
Sipu Wang · 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022) · 2022
With the continuous development of society, indoor monitoring is increasingly widely used. At present, video surveillance mainly uses artificial real-time monitoring or post-monitoring to check indoor conditions and people. This paper describes the components and working principle of YOLOv5, and uses YOLOv5 algorithm to conduct network simulation and training for indoor common targets. Through the indoor photography head to collect the scene, using YOLOv5 algorithm to process the original photos, when there is an abnormal person in the image, the system will issue an alarm to remind the owner to deal with it in time. The experiment shows that the system can respond the indoor situation economically, quickly and effectively and meet the demand of indoor anti-theft.