# Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos
## 1 Introduction  
Crime is a deed that is based on an offensive act, but to overcome such offensive acts it has always been necessary to utilize different means to minimize them in short time. Some of these crimes result in danger to both the environment and human life. Every country in the world seeks peace because it enables societies to flourish, and economies to grow and achieve new heights of success over time. Contrary to this, an unpeecful environment full of illegal activities brings the downfall of societies, communities, and countries. For example, wars have the biggest impact on a society in which the whole country suffers from huge pain and loss. Peaceful people are forced to fight, which ends with the loss of life, property, and identity. Some people are forced to relocate and travel to other countries asrefugees. Therefore, countries with good defense systems can protect their sovereignty and help others to live peaceful lives. Similarly, on a lower scale, people who are not educated or are mistreated by society try to take the law into their own hands and commit crimes such as robberies. In robberies, robbers use weapons to terrorize other humans to meet their end needs, which normally ends with a loss of life or money. In most of these events, handheld weapons like pistols are the commonly used firearms that are easy to carry and get away with. Likewise, weapons are used by terrorists against common citizens, which results in a massacre. In the past few years, the incidents involving firearms have increased in public areas. A couple of years back were the attacks on mosques in New Zealand on 15 March 2019; at 1:40 pm, the attacker attacked the Christchurch AL-Noor Mosque during a Friday prayer, killing almost 44 innocent and unarmed worshippers, and with a break of just 15 min at 1:55 pm, another attack happened that killed seven more civilians [1]. In the USA and then in Europe, incidents of active shooters have occurred, such as in Columbine High School (USA, 37 victims), [PERSON]'s assault on Uotya Island (Norway, 179 victims), or the killing of 23 people in the Charlie Hebdo newspaper attack. UNODC statistics says that among 0.1 million people in a country, the crimes involving guns are very high, i.e., 1.6 in Belgium, 4.7 in the USA, and 21.5 in Mexico [2]. The reason and motivation behind this work is to detect various weapons in real time, thus reducing the aforementioned incidents. These incidents can be controlled using an early alarm system by alerting the operators and concerned authorities so that action can be taken immediately. For decades, law enforcement agencies have installed CCTV cameras for surveillance that have helped them in noticing illegal activities in streets, airports, etc., and utilize the video as a piece of evidence in a court of law. People install CCTV cameras in their shops or markets for protection from thieves and robbers [3; 4]. One common issue that arises with CCTV cameras is that they are not intelligent, which means a person should be available round the clock for monitoring live videos to take timely action against illegal activity. This turns out to be a tiresome job when a person must monitor multiple video screens for a longer duration of time [5]. Artificial intelligence (AI) and computer vision have enabled us to utilize video feeds in a way that we can detect and classify the objects of our interest in it. Therefore, it has been widely adopted and used in many applications such as autonomous vehicles, security feeds, etc. Many algorithms and architectural works have been done for the aforementioned tasks. In 2020, [PERSON], [PERSON] et al. [6] provided a detailed and comprehensive discussion and analysis of state-of-the-art techniques and algorithms used in the field of computer vision using deep learning technology, especially for the GPU-based embedded system. They covered many state-of-the-art algorithms that were trained and tested on COCO, PASCAL VOC datasets.