Object detection results have been rapidly improved over a short period of time with the development of deep convolutional neural networks. Although impressive results have been achieved on large/medium sized objects, the performance on small objects is far from satisfactory and one of remaining open challenges is detecting small object in unconstrained conditions (e.g. COCO and WIDER FACE benchmarks). The reason is that small objects usually lack sufficient detailed appearance information, which can distinguish them from the backgrounds or similar objects. To deal with the small object detection problem, in this paper, we propose an end-to-end multi-task generative adversarial network (MTGAN), which is a general framework. In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection. The discriminator is a multi-task network, which describes each inputted image patch with a real/fake score, object category scores, and bounding box regression offsets. Furthermore, to make the generator recover more details for easier detection, the classification and regression losses in the discriminator are back-propagated into the generator during training process. Extensive experiments on the challenging COCO and WIDER FACE datasets demonstrate the effectiveness of the proposed method in restoring a clear super-resolved image from a blurred small one, and show that the detection performance, especially for small sized objects, improves over state-of-the-art methods by a large margin.

中文翻译：

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用于检测野外小物体的多任务生成对抗网络

随着深度卷积神经网络的发展，目标检测结果在短时间内得到了迅速提升。尽管在大/中尺寸物体上取得了令人印象深刻的结果，但在小物体上的性能远不能令人满意，剩下的开放挑战之一是在无约束条件下检测小物体（例如 COCO 和 WIDER FACE 基准）。原因是小物体通常缺乏足够详细的外观信息，可以将它们与背景或类似物体区分开来。为了解决小物体检测问题，在本文中，我们提出了一种端到端的多任务生成对抗网络（MTGAN），这是一个通用框架。在 MTGAN 中，生成器是一个超分辨率网络，它可以将小的模糊图像上采样为精细尺度的图像，并恢复详细信息以进行更准确的检测。鉴别器是一个多任务网络，它用真/假分数、对象类别分数和边界框回归偏移量来描述每个输入的图像块。此外，为了使生成器恢复更多细节以便于检测，在训练过程中将鉴别器中的分类和回归损失反向传播到生成器中。在具有挑战性的 COCO 和 WIDER FACE 数据集上进行的大量实验证明了所提出的方法在从模糊的小图像恢复清晰的超分辨率图像方面的有效性，并表明检测性能，尤其是对于小尺寸物体，比 state-of-大幅度提高了最先进的方法。