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 sharedand 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度复杂 ISAR 目标检测


逆合成孔径雷达（ISAR）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，基于高级区域提议网络（ARPN）和弱半监督深度学习，提出了一种快速高效的弱半监督方法，称为深度 ISAR 目标检测（DIOD）。联合稀疏学习：1）为了生成高级区域建议并在最短的时间内鲁棒而准确地定位潜在的 ISAR 对象，基于多尺度全卷积区域建议网络和区域建议分类和排序策略提出了 ARPN。 ARPN 与基于 Inception-ResNet 的系统共享通用的卷积层，并提供几乎免费的提案计算，性能优异； 2）针对缺乏足够标注训练数据的难题，特别是在ISAR领域，针对弱标注和未标注的ISAR图像，提出了一种方便高效的弱半监督训练方法。特别是，成对排名损失处理弱注释图像，而三元组排名损失则用于利用未注释的图像； 3）为了进一步提高整个系统的准确性和速度，引入了一种新颖的共享个体机制和关系正则化联合稀疏学习策略，以在学习共享特征和个体特征及其相关性的同时实现更具辨别力和更全面的表示。在两个真实世界的 ISAR 数据集上进行了大量实验，结果表明 DIOD 优于现有的最先进方法，并以更短的执行时间实现了更高的精度。