An Enhanced Forensic Analysis and Security Surveillance Using Deep Reinforcement Learning

Crime prevention relies on forensic analysis and security surveillance. Manual inspection and human skill are used in crime scene object detection and identification, which is time-consuming and subjective. This study proposes deep reinforcement learning for crime scene object detection to improve forensic analysis and security monitoring. Deep reinforcement learning, specifically Deep Q-Networks (DQNs), automates crime scene object detection and identification. We enable an agent to discover and classify things of interest by teaching it to learn from its environment. We use real crime scene photos and computer vision-generated data to train the DQN-based agent. Synthetic data augmentation strengthens and generalises the training model. A incentive mechanism encourages the agent to focus on important things and ignore background elements. We use a large crime scene image dataset to test our approach. Our deep reinforcement learning-based object identification approach is compared against Faster R-CNN and YOLO. Precision, recall, and F1-score assess object detecting accuracy and efficacy. Our experimental results show that our proposed approach outperforms standard object detection algorithms. The deep reinforcement learning-based agent can effectively identify crime scene objects of interest with higher precision, recall, and F1-score. This accuracy enhancement could boost forensic analysis and security surveillance, aiding law enforcement in crime investigation and prevention. Our technique handles complex and diverse crime scene scenarios. It accurately detects firearms, suspicious shipments, and other evidence. This research advances object detection approaches in forensic analysis and security monitoring, giving promising future developments.

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