ENHANCING WEAPON DETECTION IN SURVEILLANCE SYSTEMS USING DEEP LEARNING: A Comprehensive Review

Abstract

The approach of weapon detection in surveillance systems is very crucial. It makes sure that public safety is maintained in different types of areas that are of high risk. These may include airports, schools as well as other public spaces. There are different types of traditional methods for weapon detection including handcrafted feature-based approaches. But these methods are sometimes noted to lack accuracy as well as efficiency in the matter of real-time detection. Deep learning has made significant improvements in the field of weapon detection. It has been done with the help of promoting automatic feature extraction as well as precise classification. This review explores different types of deep learning techniques including various CNN-based models like YOLO, Faster R-CNN as well as SSD. The review has also examined recent advancements in weapon detection including transformers and self-supervised learning. Some critical challenges came to notice and were discussed carefully. These challenges include dataset bias, computational demands, ethical concerns as well as adversarial attacks. The review paper suggests that future research needs to keep the focus on lightweight AI models, edge computing as well as multi-modal surveillance. These approaches are expected to increase the accuracy and real-time performance and reduce the existing limitations.