Efficient weapon detection using convolutional and transformer-based Deep Learning Models

Detecting weapons in public spaces remains a significant challenge in computer vision and public safety applications. While deep learning models have achieved great progress in general object detection, there is still a lack of focused studies on class-specific detection tasks, in particular those using new architectures such as transformers. In this work, a comprehensive evaluation of the state-of-the-art deep learning object detection approaches is conducted, including convolution and transformer-based architectures. Therefore, a dedicated large-scale dataset that combines images from multiple public sources is introduced, with a focus on three main weapons categories, enabling a more targeted evaluation. Furthermore, in the paper, the effectiveness of the best-performing architecture is further improved with proposed modifications, including architectural changes and determining a suitable loss function. Finally, the obtained detection approach achieves superior detection results, as evidenced by all performance criteria.