10 April 2018 Feature-fused SSD: fast detection for small objects
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106151E (2018) https://doi.org/10.1117/12.2304811
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCAL VOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some small objects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS.
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Guimei Cao, Guimei Cao, Xuemei Xie, Xuemei Xie, Wenzhe Yang, Wenzhe Yang, Quan Liao, Quan Liao, Guangming Shi, Guangming Shi, Jinjian Wu, Jinjian Wu, } "Feature-fused SSD: fast detection for small objects", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151E (10 April 2018); doi: 10.1117/12.2304811; https://doi.org/10.1117/12.2304811

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