PREVENTING POTENTIAL ROBBERY CRIMES USING DEEP LEARNING ALGORITHM OF DATA PROCESSING

Recently, deep learning technologies, namely Neural Networks [1], are attracting more and attention from businesses the scientific community, as they help optimize processes find real solutions to problems much efficiently economically than many other approaches. In particular, well suited for situations when you need detect objects or look similar patterns in videos images, making them relevant field of information measurement technologies mechatronics robotics. With increasing number robbed apartments houses every year, addressing this issue has become one highest priorities today's society. By leveraging techniques, such Networks, robotics, innovative can be developed enhance security systems, enabling effective detection prevention apartment crimes. To evaluate performance our trained network, we conducted extensive experiments on a separate test dataset that was distinct training data. We meticulously labeled obtain accurate ground truth annotations comparison. measuring precision scores, determined effectiveness model detecting potential Our yielded an accuracy rate 97% This achievement demonstrates capability YOLO network accurately identifying criminal activities. The high indicates system effectively assist property protection efforts, providing valuable tool personnel law enforcement agencies.