Abstract
The field of computer vision stands to benefit significantly from automated crime scene detection. In this work, we demonstrate the application of DNN (Deep Neural Network) to identify a knife, blood, and gun in a picture and forecast whether a crime has occurred. Improving the accuracy of the system's detection capabilities was a top priority for us since we wanted to make the system as helpful as possible. To get a detection result, DNN's Non-linearity ReLu (Rectified Linear Unit), a Convolutional Neural Network Layer, a Fully Connected Layer, and a Dropout Function are employed. Additionally, this research makes use of a Dropout Function. To get the outcomes we want from Neural Networks, we use the open-source platform TensorFlow. Our system has a test accuracy of about 92.1% for the datasets that are currently accessible, which puts it in a very competitive position compared to other systems specifically created for this task.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
