Arc Lab.'s Blog

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분 - Page 5 For traffic sign classification, the GTSRB [27] and TT100K [32] datasets are used. For the GTSRB→TT100k scenario, we train the model on GTSRB and report th..

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분 아래의 논문과 동일. GTSDB 데이터셋에 대해 추가로 실험. Focal Loss를 통한 큰 성능 향상. https://arclab.tistory.com/277?category=759685 [***]Small Object Detection Using Deep Feature Pyramid Net..

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분 -Page 1 In comparison with normal detection tasks, the traffic signs oc- cupy small proportion of each image in the real-driving scenario [7]. -Page 3 There are thr..

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분 -Page 5 -Page 8 We evaluated our method on the Tsinghua–Tencent 100K test dataset. It achieved 87.0% mAP at a Jaccard similarity coefficient of 0.5 and the average ..

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분 -Page 2 We are the first to present a network that performs joint traffic light and sign detection. Our architecture is suitable for autonomous car deployment becau..

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분 - Page 4-5 To detect small objects from a high-resolution image, we design a two-stage architecture as shown in Fig. 3. For region proposal stage, two top-down feat..

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분 - Page 1-2 - Page 4 To verify the effectiveness of our framework, we perform experiments on Tsinghua-Tencent 100K traffic sign dataset [22].

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분