Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

中文翻译：

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基于FAR可控深林模型的多特征融合海面弱目标检测

海面目标检测一直是备受关注的问题，弱目标的检测是最困难的问题之一，也是该问题下的关键问题。诸如成像之类的传统技术无法有效检测这些类型的目标，因此研究人员选择从挖掘接收到的回波和其他方面的特征开始进行目标检测。本文提出了一种基于六维特征空间的误报率（FAR）可控的深林模型，用于高效，准确地检测海面弱目标。这是该领域中对深林模型的首次尝试。在IPIX数据上验证了该模型的有效性，并将检测概率与其他提出的方法进行了比较。在相同的FAR条件下，该方法的平均检测准确率可以达到99.19％以上，比目前最先进的方法（K-NN FAR控制的检测器）提高了9.96％。实验结果表明，多特征融合和合适的检测框架的使用对海面弱目标的检测有积极作用。