Detection of Crime Scene Objects for Evidence Analysis Using Deep Learning (DL) Techniques

Abstract

The proposed invention introduces an advanced system for detecting and analyzing crime scene objects using Deep Learning (DL) techniques, specifically designed to support forensic investigations. The system utilizes Convolutional Neural Networks (CNNs) to process visual data from crime scenes, automatically identifying and classifying various types of evidence such as weapons, fingerprints, and other trace materials. By employing cutting-edge object detection algorithms, it efficiently analyzes large volumes of image and video data, accurately distinguishing between relevant evidence and irrelevant items. The system significantly reduces human error, accelerates the identification of key objects, and provides law enforcement with reliable, real-time forensic analysis tools. It continuously improves through ongoing learning, adapting to new crime patterns and evolving types of evidence. With its ability to streamline the evidence analysis process, this invention enhances the effectiveness and speed of criminal

Specification

Description:The field of invention of the proposed system pertains to the application of Deep Learning (DL) techniques in the detection and analysis of crime scene objects for forensic investigations. Specifically, the system leverages advanced computer vision algorithms and deep neural networks to automatically identify and classify objects relevant to criminal investigations, such as weapons, bloodstains, fingerprints, or other physical evidence present at a crime scene. The system aims to enhance the efficiency, accuracy, and speed of forensic analyses, assisting law enforcement agencies and forensic experts in the process of crime scene investigation. By automating the detection process, the system reduces human error and the time required for manual analysis, allowing for a more thorough and systematic investigation. Furthermore, it supports the integration of various image modalities, including still images, videos, and 3D scans, to provide a comprehensive analysis of crime scenes. The proposed system also utilizes , Claims:1. The system is designed to detect and classify crime scene objects using Deep Learning techniques, processing images or video footage through Convolutional Neural Networks (CNNs) to automatically identify evidence such as weapons, fingerprints, and other materials.
2. The method involves training a deep learning model on a large dataset of labeled crime scene images, allowing the system to accurately recognize and categorize objects in real-time, even in complex or cluttered environments.
3. The system incorporates an object detection algorithm that identifies specific objects within the captured images, providing spatial coordinates for further analysis, which helps investigators pinpoint key pieces of evidence quickly.
4. Image pre-processing techniques are employed to enhance the quality of the visual data, removing noise and normalizing lighting conditions, thereby improving the deep learning model's performance in a variety of crime scene environments.
5. The system includes an object filtering mechani

Documents