Preventing potential robbery crimes using deep learning algorithm of data processing

Abstract

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