SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network

Yancheng Bai, Yongqiang Zhang, Mingli Ding, Bernard Ghanem; The European Conference on Computer Vision (ECCV), 2018, pp. 206-221

Object detection is a fundamental and important problem in computer vision. Although impressive results have been achieved on large/medium sized objects on large-scale detection benchmarks (e.g. the COCO dataset), the performance on small objects is far from satisfaction. The reason is that small objects lack sufficient detailed appearance information, which can distinguish them from the background or similar objects. To deal with 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 multi task network, which describes each super-resolution 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-resolution image from a blurred small one, and show that the detection performance, especially for small sized objects, improves over state-of-the-art methods.