Inverse synthetic aperture radar (ISAR) object detection is one of the most important and challenging problems in computer vision tasks. To provide a convenient and high-quality ISAR object detection method, a fast and efficient weakly semi-supervised method, called deep ISAR object detection (DIOD), is proposed, based on advanced region proposal networks (ARPNs) and weakly semi-supervised deep joint sparse learning: 1) to generate high-level region proposals and localize potential ISAR objects robustly and accurately in minimal time, ARPN is proposed based on a multiscale fully convolutional region proposal network and a region proposal classification and ranking strategy. ARPN shares common convolutional layers with the Inception-ResNet-based system and offers almost cost-free proposal computation with excellent performance; 2) to solve the difficult problem of the lack of sufficient annotated training data, especially in the ISAR field, a convenient and efficient weakly semi-supervised training method is proposed with the weakly annotated and unannotated ISAR images. Particularly, a pairwise-ranking loss handles the weakly annotated images, while a triplet-ranking loss is employed to harness the unannotated images; and 3) to further improve the accuracy and speed of the whole system, a novel sharable-individual mechanism and a relational-regularized joint sparse learning strategy are introduced to achieve more discriminative and comprehensive representations while learning the shared- and individual-features and their correlations. Extensive experiments are performed on two real-world ISAR datasets, showing that DIOD outperforms existing state-of-the-art methods and achieves higher accuracy with shorter execution time.

中文翻译：

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DIOD：快速高效的弱半监督深层复杂ISAR目标检测

合成孔径雷达（ISAR）逆目标检测是计算机视觉任务中最重要和最具挑战性的问题之一。为了提供一种方便，高质量的ISAR目标检测方法，提出了一种基于高级区域提议网络（ARPN）和弱半监督深度的快速有效的弱半监督方法，称为深度ISAR目标检测（DIOD）。联合稀疏学习：1）为了在最短的时间内生成高层次的区域提议并可靠，准确地定位潜在的ISAR对象，基于多尺度全卷积区域提议网络以及区域提议分类和排名策略，提出了ARPN。ARPN与基于Inception-ResNet的系统共享常见的卷积层，并提供几乎免费的提案计算，且性能出色。2）为解决特别是在ISAR领域缺乏足够的带注释训练数据的难题，提出了一种对带弱注释和无注释的ISAR图像进行方便，有效的弱半监督训练方法。特别是，成对排列的损失处理弱注释的图像，而采用三重排列的排列损失来利用未注释的图像。3）为进一步提高整个系统的准确性和速度，引入了一种新颖的可共享的个体机制和一个关系正则化的联合稀疏学习策略，以在学习共享特征和个体特征及其特征的同时实现更具区分性和综合性的表示。相关性。在两个真实的ISAR数据集上进行了广泛的实验，