Efficient Weapon Detection Using Convolutional and Transformer-based Deep Learning Models
Abstract
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.
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