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. 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. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. 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 threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. 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. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. 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. This survey may help in making recommendations and improvements of c-UAV applications for the future.

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Small drones are a rising threat due to their possible misuse for illegal activities, in particular smuggling and terrorism. The project SafeShore, funded by the European Commission under the Horizon 2020 program, has launched the "drone-vs-bird detection challenge" to address one of the many technical issues arising in this context. The goal is to detect a drone appearing at some point in a video where birds may be also present: the algorithm should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds. This paper reports on the challenge proposal, evaluation, and results 1 .

The popularity of Unmanned Aerial Vehicles (UAVs) is increasing year by year and reportedly their applications hold great shares in global technology market. Yet, since UAVs can be also used for illegal actions, this raises various security issues that needs to be encountered. Towards this end, UAV detection systems have emerged to detect and further anticipate inimical drones. A very significant factor is the maximum detection range in which the system's senses can "see" an upcoming UAV. For those systems that employ optical cameras for detecting UAVs, the main issue is the accurate drone detection when it fades away into sky. This work proposes the incorporation of Super-Resolution (SR) techniques in the detection pipeline, to increase its recall capabilities. A deep SR model is utilized prior to the UAV detector to enlarge the image by a factor of 2. Both models are trained in an end-to-end manner to fully exploit the joint optimization effects. Extensive experiments demonstrate the validity of the proposed method, where potential gains in the detector's recall performance can reach up to 32.4%.