Z. J. Ding, C. J. Zhu, J. J. Wang, Y. F. Qiu, and G. Cen, Garbage classification system based on AI and IoT, presented at the 15th IEEE International Conference on Computer Science & Education, Delft, the Netherlands, 2020.
Q. X. Zhang, G. H. Lin, Y. M. Zhang, G. Xu, and J. J. Wang, Wildland forest fire smoke detection based on faster R-CNN using synthetic smoke images, Procedia engineering, vol. 221, no. 3, pp. 441–446, 2018.
H. Z. Chen, A. Chen, L. L. Xu, H. Xie, H. L. Qiao, Q. Y. Lin, and K. Cai, A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources, Agricultural Water Management, .
C. M. Han, G. F. Li, Y. X. Ding, F. L. Yan, and L. Y. Bai, Chimney detection based on Faster R-CNN and spatial analysis methods in high resolution remote sensing images, Sensors, .
G. S. Hu, H. Y. Wang, Y. Zhang, and M. Z. Wan, Detection and severity analysis of tea leaf blight based on deep learning, Computers & Electrical Engineering, .
D. Datta and S. B. Jamalmohammed, Image classification using CNN with multi-core and many-core architecture, Applications of Artificial Intelligence for Smart Technology, .
D. Zeng, S. Zhang, F. Chen, and Y. Wang, Multi-scale CNN based garbage detection of airborne hyperspectral data, IEEE Access, .
A. B. Ye, B. Pang, Y. C. Jin, and J. H. Cui, A YOLO-based neural network with VAE for intelligent garbage detection and classification, presented at the 3rd International Conference on Algorithms, Computing and Artificial Intelligence, Sanya, China, 2020.
Z. F. Nie, W. J. Duan, and X. D. Li, Domestic garbage recognition and detection based on Faster R-CNN, Journal of Physics: Conference Series, .
J. Q. Bai, S. G. Lian, Z. X. Liu, K. Wang, and D. J. Liu, Deep learning based robot for automatically picking up garbage on the grass, IEEE Transactions on Consumer Electronics, .
H. Liu, G. O. Owolab, and S. H. Kim, Automatic Classifications and Recognition for Recycled Garbage by Utilizing Deep Learning Technology, in Proc. the 2019 7th International Conference on Information Technology: IoT and Smart City, Shanghai, China, 2019, pp. 1–4.
G. Mitta, K. B. Yagnik, M. Garg, and N. C. Krishnan, Spotgarbage: Smartphone app to detect garbage using deep learning, in Proc. the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 2016, pp. 940–945.
G. Y. Jia, Y. J. Zhu, G. J. Han, S. Chan, and L. Shu, STC: An intelligent trash can system based on both NB-IoT and edge computing for smart cities, Enterprise Information Systems, vol. 14, nos. 9&10, pp. 1422–1438, 2020.
S. L. Rabano, M. K. Cabatuan, E. Sybingco, E. P. Dadios, and E. J. Calilung, Common garbage classification using mobilenet, presented at the 10th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines, 2018.
T. Ojala, M. Pietikainen, and T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
L. Zhang, R. Chu, S. Xiang, S. Liao, and S. Z. Li, Face detection based on multi-block lbp representation, presented at the International Conference on Biometrics, Seoul, Republic of Korea, 2007.
D. G. Lowe, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.
C. Tao, Y. H. Tan, H. J. Cai, and J. W. Tian, Airport detection from large IKONOS images using clustered SIFT keypoints and region information, IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 1, pp. 128–132, 2011.
N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, presented at the 22th IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 2005.
Y. W. Pang, Y. Yuan, X. L. Li, and J. Pan, Efficient HOG human detection, Signal Processing, .
A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, .
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and F. F. L, Imagenet: A large-scale hierarchical image database, presented at the 26th IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 2009.
C. Szegedy, W. Liu, Y. Q Jia,, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, Going deeper with convolutions, presented at the 32th IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015.
K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, Deep residual learning for image recognition, presented at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778.
J. Hu, L. Shen, and G. Sun, Squeeze-and-excitation networks, presented at the 35th IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018.
Y. H. Liu, Feature extraction and image recognition with convolutional neural networks, Journal of Physics: Conference Series, .
M. Jogin, Mohana, M. S. Madhulika, G. D. Divya, R. K. Meghana, and S. Apoorva, Feature extraction using convolution neural networks (CNN) and deep learning, presented at the 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 2018.
C. Shorten and T. M. Khoshgoftaar, A survey on image data augmentation for deep learning, Journal of Big Data, .
R. S. Zhang, W. Z. Quan, L. B. Fan, L. M. Hu, and L. M. Yan, Distinguishing computer-generated images from natural images using channel and pixel correlation, Journal of Computer Science and Technology, .
X. J. Zhang, Y. F. Lu, and S. H. Zhang, Multi-task learning for food identification and analysis with deep convolutional neural networks, Journal of Computer Science and Technology, .
J. G. Jia, Y. F. Zhou, X. W. Hao, F. Li, C. Desrosiers, and C. M. Zhang, Two-stream temporal convolutional networks for skeleton-based human action recognition, Journal of Computer Science and Technology, .
S. Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz, and D. Terzopoulos, Image segmentation using deep learning: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, .
Y. Wu, J. W. Lim, and M. H. Yang, Online object tracking: A benchmark, presented at the 30th IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013.
Z. F. Xie, Y. C. Guo, S. H. Zhang, W. J. Zhang, and L. Z. Ma, Multi-exposure motion estimation based on deep convolutional networks, Journal of Computer Science and Technology, .
A. Caroppo, A. Leone, and P. Siciliano, Comparison between deep learning models and traditional machine learning approaches for facial expression recognition in ageing adults, Journal of Computer Science and Technology, .
P. Wang, E. Fan, and P. Wang, Comparative analysis of image classification algorithms based on traditional machine learning and deep learning, Pattern Recognition Letters, .
S. Q. Ren, K. M. He, R. Girshick, and J. Sun, Faster R-CNN towards real-time object detection with region proposal networks, http://arxiv.org/abs/1506.01497, 2016.
K. M. He, G. Gkioxari, P. Dollár, and R. Girshick, Mask R-CNN, presented at the 16th IEEE Conference on Computer Vision (ICCV), Venice, Italy, 2017.
J. F. Dai, H. Z. Qi, Y. W. Xiong, Y. Li, G. D. Zhang, H. Hu, and Y. C. Wei, Deformable convolutional networks, presented at the 16th IEEE Conference on Computer Vision (ICCV), Venice, Italy, 2017.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, SSD: Single shot multibox detector, presented at the 14th European Conference on Computer Vision, Amsterdam, the Netherlands, 2016.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: Unified, real-time object detection, presented at the 33th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016.
J. Redmon and A. Farhadi, YOLO9000: Better, faster, stronger, presented at the 34th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017.
Q. J. Zhao, T. Sheng, Y. T. Wang, Z. Tang, Y. Chen, L. Cai, and H. B. Ling, M2det: A single-shot object detector based on multi-level feature pyramid network, in Proc. the 33th AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 2019, pp. 9259–9266.
M. X. Tan, R. M. Pang, and Q. V. Le, EfficientDet: Scalable and efficient object detection, presented at the 37th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020.
Q. Q. Chen and Q. H. Xiong, Garbage classification detection based on improved YOLOv4, Journal of Computer and Communications, .
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard, et al., Tensorflow: A system for large-scale machine learning, in Proc. the 12th USENIX Conference on Operating Systems Design and Implementation, Savannah, GA, USA, 2016, pp. 265–283.
Z. Z. Wu, S. H. Wan, X. F. Wang, M. Tan, L. Zou, X. L. Li, and Y. Chen, A benchmark data set for aircraft type recognition from remote sensing images, Applied Soft Computing, .
M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman, The pascal visual object classes (VOC) challenge, International Journal of Computer Vision, .