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http://dx.doi.org/10.14372/IEMEK.2017.12.4.231

A Lightweight Real-Time Small IR Target Detection Algorithm to Reduce Scale-Invariant Computational Overhead  

Ban, Jong-Hee (Department of Embedded Systems Engineering from Daegu University)
Yoo, Joonhyuk (College of Computer & Communication Engineering at Daegu University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.12, no.4, 2017 , pp. 231-238 More about this Journal
Abstract
Detecting small infrared targets from the low-SCR images at a long distance is very hard. The previous Local Contrast Method (LCM) algorithm based on the human visual system shows a superior performance of detecting small targets by a background suppression technique through local contrast measure. However, its slow processing speed due to the heavy multi-scale processing overhead is not suitable to a variety of real-time applications. This paper presents a lightweight real-time small target detection algorithm, called by the Improved Selective Local Contrast Method (ISLCM), to reduce the scale-invariant computational overhead. The proposed ISLCM applies the improved local contrast measure to the predicted selective region so that it may have a comparable detection performance as the previous LCM while guaranteeing low scale-invariant computational load by exploiting both adaptive scale estimation and small target feature feasibility. Experimental results show that the proposed algorithm can reduce its computational overhead considerably while maintaining its detection performance compared with the previous LCM.
Keywords
Small target detection; Infrared image; Local contrast method; Scale invariant; Real-time detection;
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Times Cited By KSCI : 3  (Citation Analysis)
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