Optimizing Forensic Investigation and Security Surveillance with Deep Reinforcement Learning Techniques
Abstract
Deep Reinforcement Learning (DRL) has emerged as a useful method for improving both accuracy and efficiency in advanced forensic analysis and security surveillance. This research gives the findings of a thorough assessment of the use of DRL methodologies, providing a full picture of its performance metrics, forensic analysis capabilities, security surveillance efficacy, and computational efficiency. Our DRL model performed admirably, with an accuracy of 92% and precision, recall, and F1 score metrics demonstrating robust capability for classification tasks. The average Intersection over Union (IOU) score of 86% demonstrates its spatial awareness. When compared to previous approaches, our model had a much higher detection rate (95%) while keeping a low false positive rate (3%). The hybrid strategy produced a balanced performance, with a detection rate of 92%, by using the capabilities of both DRL and classical techniques. Our DRL model consistently outperformed 90% detection rates in various surveillance scenarios, including indoor, outdoor, and night-time environments, while retaining a low false alarm rate. This demonstrates its flexibility and dependability in real-world security applications. Despite the complexity of DRL models, our model’s training period was 48 hours, and the inference time for real-time analysis was only 15 milliseconds per frame. These gains demonstrate the utility of our technique for use in dynamic security surveillance systems.