Electro-optro cameras are generally cheap, though thermal cameras are more expensive, but both are sensitive to environmental settings. Both electro-optro and thermal cameras offer high classification capabilities with accurate localization and ranging, when multiple sensors are deployed. Non-automatic systems that include end users who monitor and confirm the classification label of the detected target are usually the best performing in classification capabilities but have generally a high operational cost due to training of personnel and system maintainance. In Figure 1, a general comparison between individual components of such systems in terms of detectable range, localization accuracy, classification capabilities, multi-target extension, and operational conditions with respect to environmental settings and price is presented. Unfortunately, the majority of these systems are commercial applications and elaborating on their specifications would go beyond the purpose of this work. These applications include multiple integrated sensors for detecting the threat, mainly through radar and/or electro-optical/thermal (EO-IR) sensors and less commonly through acoustic and radio frequency (RF) sensors. Recent advances in counter UAV (c-UAV) solutions offer systems that comprise a multi-sensory arsenal in an effort to robustly maintain situational awareness and protect a critical infrastructure or an important event. Prisons, airports, sporting venues, public buildings, and other sensitive sites are at serious risk, and correctly understanding the multitude of challenges that UAVs present is central for the effective protection of critical infrastructures and citizens. Reports estimated that this incident alone cost ВЈ 1.4 m and affected the lives of many passengers. On December 2018, a UAV was spotted flying close to Gatwick airport ultimately causing the closure of BritainвЂ™s second largest airport for 36 h and disrupting 1000 flights. In recent years, newspapers and mass media have reported dozens of incidents involving UAVs flying over restricted areas and around critical infrastructures or during public events. However, the rapid spread of UAVs is generating serious security issues. Today, UAVs are used from government authorities for tasks such as border security, law enforcement, and wildfire surveillance to commercial related tasks used by civilians such as construction, agriculture, insurance, internet communications, and general cinematography. Recent reports confirm the proliferation of UAV production worldwide. UAVs have proven to be both autonomous and flexible in a variety of environments and tasks and they bring a continuous market increase in a growing number of useful applications. Unmanned Aerial Vehicles (UAVs) or Systems (UAS) (The terms UAVs, UAS, and drones are equivalently used in this document) are becoming a part of citizensвЂ™ everyday life. This survey may help in making recommendations and improvements of c-UAV applications for the future. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threatвЂ™s identification. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications.

However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks.

Multi counter application