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 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.
|Original language||English (US)|
|Title of host publication||Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings|
|Editors||Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert|
|Number of pages||17|
|State||Published - 2018|
|Event||15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany|
Duration: Sep 8 2018 → Sep 14 2018
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||15th European Conference on Computer Vision, ECCV 2018|
|Period||09/8/18 → 09/14/18|
Bibliographical noteFunding Information:
Acknowledgments. This 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.
© Springer Nature Switzerland AG 2018.
- Generative adversarial network
- Small object detection
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)