SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network
KAUST DepartmentVisual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2018-10-06
Print Publication Date2018
Permanent link to this recordhttp://hdl.handle.net/10754/630628
MetadataShow full item record
AbstractObject 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 multi-task network, which describes each super-resolved 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. 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.
CitationBai Y, Zhang Y, Ding M, Ghanem B (2018) SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network. Lecture Notes in Computer Science: 210–226. Available: http://dx.doi.org/10.1007/978-3-030-01261-8_13.
SponsorsThis work was supported mainly by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research and by Natural Science Foundation of China, Grant No. 61603372.
Conference/Event name15th European Conference on Computer Vision, ECCV 2018