Anchor pruning for object detection

Computer Vision and Image Understanding - Tập 221 - Trang 103445 - 2022
Maxim Bonnaerens1, Matthias Freiberger1, Joni Dambre1
1IDLab-AIRO, Ghent University - imec, Gent, Oost Vlaanderen, Belgium

Tài liệu tham khảo

Cai, Z., Vasconcelos, N., 2018. Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154–6162. Cai, L., Zhao, B., Wang, Z., Lin, J., Foo, C.S., Aly, M.S., Chandrasekhar, V., 2019. MaxpoolNMS: getting rid of NMS bottlenecks in two-stage object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9356–9364. Chen, 2019 Deng, 2020, Model compression and hardware acceleration for neural networks: A comprehensive survey, Proc. IEEE, 108, 485, 10.1109/JPROC.2020.2976475 Everingham, 2010, The pascal visual object classes (voc) challenge, Int. J. Comput. Vis., 88, 303, 10.1007/s11263-009-0275-4 Girshick, R., Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448. He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. Hinton, 2015 Hu, J., Shen, L., Sun, G., 2018. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141. Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al., 2017. Speed/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7310–7311. Ke, W., Zhang, T., Huang, Z., Ye, Q., Liu, J., Huang, D., 2020. Multiple Anchor Learning for Visual Object Detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10206–10215. Law, H., Deng, J., 2018. Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750. Li, 2016 Li, S., Yang, L., Huang, J., Hua, X.-S., Zhang, L., 2019. Dynamic anchor feature selection for single-shot object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6609–6618. Liao, 2018, Textboxes++: A single-shot oriented scene text detector, IEEE Trans. Image Process., 27, 3676, 10.1109/TIP.2018.2825107 Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S., 2017a. Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P., 2017b. Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988. Lin, 2014, Microsoft coco: Common objects in context, 740 Liu, 2016, Ssd: Single shot multibox detector, 21 Liu, 2018 Luo, 2016, Understanding the effective receptive field in deep convolutional neural networks, 4898 Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788. Redmon, J., Farhadi, A., 2017. YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271. Redmon, 2018 Ren, 2015, Faster r-cnn: Towards real-time object detection with region proposal networks, 91 Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 510–4520. Sermanet, 2013 Simonyan, 2014 Tan, M., Pang, R., Le, Q.V., 2020. Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790. Tian, Z., Shen, C., Chen, H., He, T., 2019. Fcos: Fully convolutional one-stage object detection. in: Proceedings of the IEEE International Conference on Computer Vision, pp. 9627–9636. Verucchi, 2020, A systematic assessment of embedded neural networks for object detection, 937 Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D., 2019. Region proposal by guided anchoring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2965–2974. Yang, 2018, Metaanchor: Learning to detect objects with customized anchors, 320 Zhang, 2019, Freeanchor: Learning to match anchors for visual object detection, 147 Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z., 2018. Single-shot refinement neural network for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4203–4212. Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z., 2017. S3fd: Single shot scale-invariant face detector. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 192–201. Zhong, 2020, Anchor box optimization for object detection, 1286 Zhou, 2017 Zhu, 2017