Towards Improving Real-Time Surveillance Systems with Enhancement Techniques for Deep Learning Models

In the context of increasing number of public threats, the development of automated real-time detection systems, capable of identifying crime activity and potential attacks in video recordings, has become a major priority. We propose accurate and efficient deep learning solutions for analyzing surveillance camera footage and signaling situations of imminent threat, based on detecting specific elements, such as theft masks and various types of weapons. We showcase the enhancement of YOLOv8 model with an attention mechanism, based on Squeeze-and-Excitation (SE), and with deformable convolution layers. The model is trained on custom datasets with optimization algorithms like Adam, AdamW and SGD. The performance tests are employed on a separate dataset, analyzing various specific metrics. Furthermore, a comparative analysis with alternative deep learning models, such as RetinaNet and Faster R-CNN, highlights the advantages and disadvantages of each model, providing insights into their performance under various conditions. Additionally, a voting mechanism is implemented to manage discrepancies in detections from multiple models. Extensive experimental results demonstrate significant improvements in detection precision and efficiency, making the proposed enhancements a viable approach for more accurate threat detection in real-time surveillance applications, while demonstrating our model's superiority in balancing detection speed and precision.