# Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos
## 2 Related Work
The work presented in [20] utilized three different CNN-based models for weapon detection, namely Faster R-CNN, Retina Net, and YOLOv3; along with it they added pose estimation to further enhance the model. The authors compared these models in terms of different performance metrics such as precision, recall, and F1 score, in which YOLOv3 showed good results but the rate of false positives was high. This approach lacks real-time capabilities and has poor performance issues on embedded devices. Furthermore, a model that was trained on only 1220 images does not cover all the real-time scenarios, such as, for example, images in different backgrounds and orientations for the generalized solution. The study of [21] shows the use of the transfer learning approach on AlexNet, VGG16, and VGG19 models to train gun and knife detection from images. The results show good accuracy, but accuracy is not a good metric to stand out the results in object detection models where mean average precision is preferred. Likewise, the model lacks the capabilities to be deployed in a real-time environment. In reference [22], the authors trained a model on a public dataset that used the open pose methodology for pose estimation that aids in weapon detection and improves the weapon detectors' detection capability. The authors of [23] identified that there is a lack of datasets in weapon detection problems; therefore, they proposed a way to generate synthetic datasets using a graphics engine. They have added an anomaly detector as well to ensure a reduction in the false-positive rate; however, the created dataset does not help much in the reduction in false positives but the anomaly detector helps in reducing it. In reference [24], the authors developed a weapon detector based on two models and compared them. They utilized a multi-contrast convolutional neural networks (MC-CNN) model and faster region-based convolutional neural networks (Faster R-CNN) model for concealed weapon detection. They have found that MC-CNN has a more complex architecture than the Faster R-CNN model; therefore, it performs much better than it. However, they developed a small, customized dataset for it that does not cover the real-time scenarios; moreover, the model is heavier for real-time scenarios and therefore not recommended for utilization in the real world. In work [25], the authors proposed a novel method for improving hand-held firearms detection. They have extracted the human hand and combined it with the human pose by using the open pose method to train the classifier that detects whether the person is holding a weapon or not. If both pose and hand suggest that the person is holding a weapon, the model classifies it as a weapon detected. Their work is only a recommendation that can assist in a better weapon detection model; therefore, it is not good for any real-time system and is prone to false positives. In reference [26], the authors utilized the YOLOv4 algorithm for weapon detection trained on the Google and Kaggle datasets. They used the internet of things (IoT) approach to collect video and fed it to their model. The method they have used is good but the dataset is not great because it does not cater to all real-time scenarios with different image qualities and backgrounds that affect the mAP of the model. The study of [27] shows the comparison of two different weapon detection models, YOLOv3 and YOLOv4, trained on images collected from Google. They have shown that YOLOv4 outperforms the YOLOv3 model in terms of processing time and sensitivity of detection because the YOLOv4 algorithm is a superior model to YOLOv3. The researcher of [28] used Alex-net in combination with some other techniques such as spatial pyramid pooling (spp) to train a weapon detection model. However, this approach is quite old and poor since many state-of-the-art algorithms outperform Alex-net now; therefore, it is not suitable for real-time systems.