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A fast object detection method based on object region dissimilarity and 1-D AGADM (one dimensional average gray absolute difference maximum) between object and background is proposed for real-time defection of small offshore targets.Then computational complexity,antinoise performance,the signal-to-noise ratio (SNR) gain between original images and their results as a function of SNR of original images and receiver operating characteristic (ROC) curve are analyzed and compared with those existing methods of small target detection such as two dimensional average gray absolute difference maximum (2-D AGADM),median contrast filter algorithm and multi-level filter algorithm.Experimental results and theoretical analysis have shown that the proposed method has faster speed and more adaptability to small object shape and also yields improved SNR performance.
A fast object detection method based on object region dissimilarity and 1-D AGADM (one dimensional average gray absolute difference maximum) between object and background is proposed for real-time defection of small offshore targets. If computational complexity, antinoise performance, the signal- to-noise ratio (SNR) gain between original images and their results as a function of SNR of original images and receiver operating characteristic (rOC) curves are analyzed and compared with those existing methods of small target detection such as two dimensional average gray absolute difference maximum (2-D AGADM), median contrast filter algorithm and multi-level filter algorithm. Experimental results and theoretical analysis have shown that the proposed method has faster speed and more adaptability to small object shape and also yield improved SNR performance.