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Novel up-scale feature aggregation for object detection in aerial images
Lin,Hu1; Zhou,Jingkai1; Gan,Yanfen2; Vong,Chi Man3; Liu,Qiong1
Source PublicationNeurocomputing

Object detection is a pivotal task for many unmanned aerial vehicle (UAV) applications. Compared to general scenes, the objects in aerial images are typically much smaller. For this reason, most general object detectors suffer from two critical challenges while dealing with aerial images: 1) The widely exploited Feature Pyramid Network works by integrating high-level features to lower levels progressively. However, this manner does not transfer equivalent information from each level of backbone network to the generated features, and the shared detection head faces an unbalanced sources of information flow, damaging the detection accuracy. 2) Up-sampling is commonly used to expand feature resolution for feature fusion or feature aggregation. However, existing up-sampling methods are ineffective to reconstruct high resolution feature maps. To address these two challenges, two works are proposed: 1) An up-scale feature aggregation framework that fully utilizes multi-scale complementary information, and 2) a novel up-sampling method that further improve detection accuracy. These two proposals are integrated into an end-to-end single-stage object detector namely HawkNet. Extensive experiments are conducted on VisDrone-DET2018, UAVDT and DIOR datasets. Compared to the RetinaNet baseline, our HawkNet achieves absolute gains of 6.0%, 1.2% and 5.9% in average precision (AP) on VisDrone-DET2018, UAVDT and DIOR datasets, respectively. For a 800 × 1333 input on the UAVDT dataset, HawkNet with ResNet-50 backbone surpasses existing methods for single-scale inference and achieves the best performance (37.4 AP), while operating at 10.6 frames per second on a single Nvidia GTX 1080Ti GPU.

KeywordAerial Images Feature Aggregation Object Detection Up-sampling
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000571895700016
Scopus ID2-s2.0-85087329363
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Cited Times [WOS]:10   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Corresponding AuthorLiu,Qiong
Affiliation1.South China University of Technology,Guangzhou,510006,China
2.South China Business College,Guangdong University of Foreign Studies,Guangzhou,510545,China
3.University of Macau,Macau,999078,China
Recommended Citation
GB/T 7714
Lin,Hu,Zhou,Jingkai,Gan,Yanfen,et al. Novel up-scale feature aggregation for object detection in aerial images[J]. Neurocomputing,2020,411:364-374.
APA Lin,Hu,Zhou,Jingkai,Gan,Yanfen,Vong,Chi Man,&Liu,Qiong.(2020).Novel up-scale feature aggregation for object detection in aerial images.Neurocomputing,411,364-374.
MLA Lin,Hu,et al."Novel up-scale feature aggregation for object detection in aerial images".Neurocomputing 411(2020):364-374.
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