Accurate Automatic Object Identification Under Complex Lighting Conditions via AI Vision on Enhanced Infrared Polarization Images

Object identification (OI) is widely used in fields like autonomous driving, security, robotics, environmental monitoring, and medical diagnostics. OI using infrared (IR) images provides high visibility in low light for all-day operation compared to visible light. However, the low contrast often causes OI failure in complex scenes with similar target and background temperatures. Therefore, there is a stringent requirement to enhance IR image contrast for accurate OI, and it is ideal to develop a fully automatic process for identifying objects in IR images under any lighting condition, especially in photon-deficient conditions. Here, we demonstrate for the first time a highly accurate automatic IR OI process based on the combination of polarization IR imaging and artificial intelligence (AI) vision (Yolov7), which can quickly identify objects with a high discrimination confidence level (DCL, up to 0.96). In addition, we demonstrate that it is possible to achieve accurate IR OI in complex environments, such as photon-deficient, foggy conditions, and opaque-covered objects with a high DCL. Finally, through training the model, we can identify any object. In this paper, we use a UAV as an example to conduct experiments, further expanding the capabilities of this method. Therefore, our method enables broad OI applications with high all-day performance.