Infrared Small UAV Target Detection via Isolation Forest

The illegal misuse of noncooperative unmanned aerial vehicles (UAVs) poses huge threats to society and life safety. Infrared imaging is reliable to monitor UAVs and the anti-UAVs technology via infrared images has attracted more and more attention. In order to provide sufficient time for follow-up, UAVs are acquired at long distances, usually exhibiting the features of weak and small. Furthermore, infrared images are usually with low signal-to-clutter ratio (SCR). These factors make the correct detection of UAVs a challenge. Existing methods do not fully exploit the phenomenon that the UAVs are easily isolated, resulting in unsatisfactory detection results. For alleviating the issue, a novel detection method via isolation forest (iForest) is proposed. In the proposed method, the multidirection couple-order derivative properties are first analyzed, which enlarges the feature difference between UAVs and background. Then, a global iForest is constructed, which takes full advantage of the phenomenon that UAVs are susceptible to being isolated. As far as we know, this is the first time that iForest is constructed in an infrared small targets detection field. Furthermore, a local iForest is created, which further eliminates the residual false alarms of the result of global iForest. Experiments on nine sequences demonstrate the performance of the proposed method, which is capable of detecting various UAVs under diverse backgrounds.