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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.3
- 수록면
- 416 - 422 (7page)
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초록· 키워드
오류제보하기
Due to the large differences in human activity within classes, the large similarity between classes, and the problems of visual angle and occlusion, it is difficult to extract features manually, and the detection rate of human behavior is low. In order to better solve these problems, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multi-object recognition and localization through a second-order detection network, and replaces the original feature extraction module with Dense-Net, which can fuse multi-level feature information, increase network depth and avoid disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects, and enhancing the network detection accuracy under multiple objects. During the experiment, the improved Faster R-CNN method in this article has 84.7% target detection result, which is improved compared to other methods, which proves that the target recognition method has significant advantages and potential.
목차
ABSTRACT
1. INTRODUCTION
2. FASTER R-CNN ARCHITECTURES
3. IMPROVED FASTER R-CNN
4. EXPERIMENTAL ANALYSIS
5. CONCLUSION
REFERENCE
참고문헌 (17)
참고문헌 신청
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, Issue 1, pp. 142-158, 2016.
L. Hang, Statistical learning method, Beijing, Tsinghua University Press, pp. 36-58, 2012.
Z. Wenda, X. Yuelei, and N. Jiacheng, “Image Target Recognition Method Based on Multi-Scale Block Convolutional Neural Network,” J ournal of Computer Applications, Vol. 36, No. 4, pp. 1033-1038, 2016.
R. Girshick, “Fast R-CNN,” Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile, pp. 1440-1448, 2015.
R. Shaoqing, H. Kaiming, and R. Girshick, “Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks,“ Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Canada, pp. 91-99, 2015.
L. Hang, Statistical learning method, Beijing, Tsinghua University Press, pp. 36-58, 2012.
Z. Wenda, X. Yuelei, and N. Jiacheng, “Image Target Recognition Method Based on Multi-Scale Block Convolutional Neural Network,” J ournal of Computer Applications, Vol. 36, No. 4, pp. 1033-1038, 2016.
R. Girshick, “Fast R-CNN,” Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile, pp. 1440-1448, 2015.
R. Shaoqing, H. Kaiming, and R. Girshick, “Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks,“ Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Canada, pp. 91-99, 2015.
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