# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 2 Literature Review  
### Weapons Detection in Security Monitoring Environments  
Numerous methods for weapons detection have been proposed, with object detection algorithms recently gaining popularity among researchers. [PERSON] et al. [6] suggested an automatic handgun detector using a Convolutional Neural Network (CNN) classifier with the Region Proposal Network (RPN) approach, confirming its effectiveness by comparing it to the Sliding Windows method. In a subsequent work [7], they proposed the use of two symmetric IP cameras to eliminate background objects, reducing the number of false positives. [PERSON] and [PERSON] [8] designed a detection scheme divided into two stages. In the first stage, they detected people using YOLO [9], and in the second stage, they detected handguns on them using a CNN. Other methods utilising the YOLO detector have been developed. [PERSON] et al. [10], using YOLOv3, created a detection system for millimetre-wave weapons. [PERSON] et al. [11] modified the YOLOv4 backbone by implementing a spatial module to enable real-time autonomous weapons detection in CCTV. [PERSON] et al. [12] proposed using Scaled-YOLOv4 for improving weapon detection and applied the TensorRT network optimiser for deployment on an edge computing device. Other studies have employed different methods. For instance, [PERSON] et al. [13] developed a real-time detector for monitoring videos that focuses on cold weapons such as kitchen knives, daggers, and machetes, among others. Additionally, the authors improved the model's robustness to varying lighting conditions by applying several pre-processing algorithms. [PERSON] et al. [14] proposed a two-level detection methodology to minimise false positive rates in handgun detection. Similarly, [PERSON] et al. [15] proposed including pose information in order to reduce the false positive rates on different models. [PERSON] et al. [16] applied the Gaussian blur algorithm to remove the image background, focusing on weapons detection and minimising both false negative and false positive rates. Likewise, [PERSON] and [PERSON] [17] applied the Gaussian deblur technique to pre-process the images and improve the features of the weapons. Furthermore, the work of [PERSON] et al. [1] played a pivotal role in this domain by providing the first publicly available database, to our knowledge, based on a CCTV environment. This contribution has significantly enhanced the performance of detectors in such scenarios, and has been used in multiple contexts even to check the computation needed in computer vision [18]. Recently, some research has followed a similar approach. On one hand, [PERSON] et al. [19] introduced a new CCTV-like outdoor dataset and utilised a tiling technique to detect small weapon objects. Similarly, [PERSON] et al. [20] presented another CCTV-like indoor dataset and deployed two models on an edge device using the same detection scheme as [PERSON] and [PERSON] [8]. Like [PERSON] and [PERSON], we advocate for applying a detection scheme. However, our approach differs, as our detection scheme is employed to support the model in more complex cases, allowing for a reduction in the number of false positives without adversely affecting the inference time. Furthermore, as can be seen in Table 1, there are few models that utilise the same dataset, and most of them do not have real-case scenarios that reflect a CCTV system. Moreover, each model uses a different experimental setup, resulting in some discrepancies. Given the heterogeneity of datasets and evaluation methods, as well as the fact that many models are not publicly available, with several datasets being private or restricted, we have created a near-real-world CCTV image dataset, of which the test subset is publicly available. The full dataset is available on request. We also provide our model's trained weights on request.