Abstract. Object detection is a fundamental and important problem
in computer vision. Although impressive results have been achieved on
large/medium sized objects in large-scale detection benchmarks (e.g. the
COCO dataset), the performance on small objects is far from satisfactory. The reason is that small objects lack sufficient detailed appearance
information, which can distinguish them from the background or similar
objects. To deal with the small object detection problem, we propose
an end-to-end multi-task generative adversarial network (MTGAN). 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 multitask network, which describes each super-resolved image patch with a
real/fake score, object category scores, and bounding box regression off-
sets. 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. Extensive experiments on the challenging COCO dataset 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.