The detection method for coal dust caused by chute discharge based on YOLOv4-tiny

Li Haibin; Sun Yuan; Zhang Wenming; Li Yaqian

doi:10.12086/oee.2021.210049

[3] Frizzi S, Kaabi R, Bouchouicha M, et al. Convolutional neural network for video fire and smoke detection[C]//IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, 2016: 877–882.

[4] Tao C Y, Zhang J, Wang P. Smoke detection based on deep convolutional neural networks[C]//2016 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), Wuhan, 2016: 150–153.

[5] Zhang F, Qin W, Liu Y B, et al. A Dual-Channel convolution neural network for image smoke detection[J]. Multimed Tools Appl, 2020, 79(45): 34587–34603.

[6] Girshick R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015: 1440–1448.

[7] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(6): 1137–1149.

[8] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016: 779–788.

[9] Redmon J, Farhadi A. YOLO9000: Better, faster, stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017: 6517–6525.

[10] Redmon J, Farhadi A. YOLOv3: An Incremental improvement[Z]. arXiv: 1804.02767, 2018.

[11] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[Z]. arXiv: 2004.10934, 2020.

[12] He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Trans Pattern Anal Mach Intell, 2015, 37(9): 1904-1916.

[13] Wang C Y, Liao H Y M, Chen P Y, et al. Enriching variety of layer-wise learning information by gradient combination[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, 2019: 2477–2484.

[14] Wang C Y, Liao H Y M, Wu Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, 2020: 1571–1580.

[15] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017: 936–944.

[16] Liu S, Qi L, Qin H F, et al. Path aggregation network for instance segmentation[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 8759-8768.

[17] Chollet F. Xception: deep learning with depthwise separable convolutions[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017: 1800-1807.

[18] Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 2015: 1-9.

[19] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016: 2818-2826.

[20] Hu J, Shen L, Sun G. Squeeze-and-Excitation networks[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 7132-7141.

[21] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Confe-rence on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016: 770-778.