Abstract 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.

中文翻译：

---

基于自动ROI提取和局部定向灰度熵对比图的小目标检测

摘要 作为目标识别中的一个特殊问题，如何快速、准确地从复杂背景中检测出小目标一直是一个悬而未决的课题。一个优秀的算法应该具有理想的 Pd（检测概率）以及较低的 Fa（误报），前提是没有漏检。以此为目标，本文提出了一个基于LDG&ECM（Local Directional Gray&Entropy Contrast Map）的增强版本。为了提高目标与背景杂波的区分度，通过将“灰度强度”与“熵”相关联来重新测量“对比度”的显着性特征。此外，通过在计算中引入“方向性”，有效去除了与目标属性相似的顽固杂波边缘。此外，为了加速算法，首先引入 cGAN（条件生成对抗网络）来自动提取 ROI（感兴趣区域）。基于DNNs，传统的依赖经验阈值的提取方法被改进为端到端的方式。另外，为了检测异质背景中的多个目标，OTSU将原始图像分割成多个同质部分。然后，设计了一种新颖的局部自适应阈值决策。通过这种方式，所有具有突出局部极值特性的真实目标都被检测出来，并且由于全局信息的平衡，避免了局部极值引起的误报。经验证，与各种场景下的最新算法相比，所提出的自动化和自适应方法具有显着的性能提升。