Journal
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2017)
Volume -, Issue -, Pages 760-768Publisher
IEEE
DOI: 10.1109/ICCVW.2017.95
Keywords
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Funding
- National Key Research and Development Program of China [2016YFB1001001]
- National Natural Science Foundation of China [61522203, 61772275, 61732007]
- Natural Science Foundation of Jiangsu Province [BK20140058, BK20170033]
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Real-time detection frameworks that typically utilize end-to-end networks to scan the entire vision range, have shown potential effectiveness in object detection. However, compared to more accurate but time-consuming frameworks, detection accuracy of existing real-time networks are still left far behind. Towards this end, this work proposes a novel CAD framework to improve detection accuracy while preserving the real-time speed. Moreover, to enhance the generalization ability of the proposed framework, we introduce maxout [1] to approximate the correlation between image pixels and network predictions. In addition, the non-maximum weighted (NMW) [2] is employed to eliminate the redundant bounding boxes that are considered as repetitive detections for the same objects. Extensive experiments are conducted on two detection benchmarks to demonstrate that the proposed framework achieves state-of-the-art performance.
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