Small target detection based on automatic ROI extraction and local directional gray&entropy contrast map

摘要

As a special problem in object recognition, how to detect the small target from complex background fast and precisely is kept an open topic. An excellent algorithm should have ideal Pd (Probability of detection) as well as lower Fa (False alarm) provided that no missing detection. Taking it as the goal, in this paper, we put forward an enhanced version based on LDG&ECM (Local Directional Gray&Entropy Contrast Map). To enhance the discrimination of the target with the background clutter, the saliency feature of "contrast" is remearsured by associating the "gray intensity" with "entropy". Also, with the introduction of "directivity" in the calculation, the stubborn clutter edge with similar property to the target is removed effectively. Moreover, to speed up the algorithm, the cGANs (Conditional Generative Adversarial Networks) is first introduced to extract ROI (Region of Interest) automatically. Based on DNNs, the traditional extraction methods relying on empirical threshold is improved to an end-to-end way. Additional, to detect the multiple targets in heterogeneous background, the original image is segmented into multiple homogeneous parts by OTSU. And then, a novel local adaptive threshold decision making is designed. By this way, all of the real targets with prominent local extreme property are detected and the false alarm caused by local extreme is avoided due to the balance of global information. It is verified that the proposed automation and adaptively approach has significant performance improvement compared with the state-of-the-art algorithms in various scenarios.