Low-Altitude Infrared Slow-Moving Small Target Detection via Spatial-Temporal Features Measure

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 14; p. 5136
• Main Authors: Mu, Jing, Rao, Junmin, Chen, Ruimin, Li, Fanming
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 08-07-2022; MDPI
• Subjects: Alarms; Altitude; Domains; False alarms; Hypothesis testing; infrared image sequences; Infrared imagery; Low altitude; low-altitude slow-moving small target; Methods; Moving targets; Signal to noise ratio; spatial-temporal features; trajectory continuity; Trajectory measurement; Unmanned aerial vehicles
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22145136

Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In this article, a new low-altitude slow-moving small target detection algorithm based on spatial-temporal features measure (STFM) is proposed. First, we construct a circular kernel to calculate the local grayscale difference (LGD) in a single image, which is essential to suppress low-frequency background and irregular edges in the spatial domain. Then, a short-term energy aggregation (SEA) mechanism with the accumulation of the moving target energy in multiple successive frames is proposed to enhance the dim target. Next, the spatial-temporal saliency map (STSM) is obtained by integrating the two above operations, and the candidate targets are segmented using an adaptive threshold mechanism from STSM. Finally, a long-term trajectory continuity (LTC) measurement is designed to confirm the real target and further eliminate false alarms. The SEA and LTC modules exploit the local inconsistency and the trajectory continuity of the moving small target in the temporal domain, respectively. Experimental results on six infrared image sequences containing different low-altitude scenes demonstrate the effectiveness of the proposed method, which performs better than the existing state-of-the-art methods.