Citation: | MAO Qinghua, LI Shikun, HU Xin, et al. Foreign object recognition of belt conveyor in coal mine based on improved YOLOv7[J]. Journal of Mine Automation,2022,48(12):26-32. DOI: 10.13272/j.issn.1671-251x.2022100011 |
[1] |
葛世荣,胡而已,裴文良. 煤矿机器人体系及关键技术[J]. 煤炭学报,2020,45(1):455-463. DOI: 10.13225/j.cnki.jccs.YG19.1478
GE Shirong,HU Eryi,PEI Wenliang. Classification system and key technology of coal mine robot[J]. Journal of China Coal Society,2020,45(1):455-463. DOI: 10.13225/j.cnki.jccs.YG19.1478
|
[2] |
方崇全. 煤矿带式输送机巡检机器人关键技术研究[J]. 煤炭科学技术,2022,50(5):263-270. DOI: 10.13199/j.cnki.cst.ZN20-056
FANG Chongquan. Research on key technology of inspection robot for coal mine belt conveyor[J]. Coal Science and Technology,2022,50(5):263-270. DOI: 10.13199/j.cnki.cst.ZN20-056
|
[3] |
吴守鹏,丁恩杰,俞啸. 基于改进FPN的输送带异物识别方法[J]. 煤矿安全,2019,50(12):127-130. DOI: 10.13347/j.cnki.mkaq.2019.12.029
WU Shoupeng,DING Enjie,YU Xiao. Foreign body identification of belt based on improved FPN[J]. Safety in Coal Mines,2019,50(12):127-130. DOI: 10.13347/j.cnki.mkaq.2019.12.029
|
[4] |
吕志强. 复杂环境下煤矿皮带运输异物图像识别研究[D]. 徐州: 中国矿业大学, 2020: 1-60.
LYU Zhiqiang. Research on foreign body image recognition of coal mine belt transport under complex environment[D]. Xuzhou: China University of Mining and Technology, 2020: 1-60.
|
[5] |
任志玲, 朱彦存. 改进CenterNet算法的煤矿皮带运输异物识别研究[J/OL]. 控制工程: 1-8[2022-09-28]. DOI: 10.14107/j. cnki. kzgc. 20200792.
REN Zhiling, ZHU Yancun. Research on foreign objects recognition of coal mine belt transportation with improved CenterNet algorithm[J/OL]. Control Engineering of China: 1-8[2022-09-28]. DOI: 10.14107/j.cnki.kzgc.20200792.
|
[6] |
胡璟皓,高妍,张红娟,等. 基于深度学习的带式输送机非煤异物识别方法[J]. 工矿自动化,2021,47(6):57-62,90. DOI: 10.13272/j.issn.1671-251x.2021020041
HU Jinghao,GAO Yan,ZHANG Hongjuan,et al. Research on the identification method of non-coal foreign object of belt conveyor based on deep learning[J]. Industry and Mine Automation,2021,47(6):57-62,90. DOI: 10.13272/j.issn.1671-251x.2021020041
|
[7] |
WANG Yuanbin,WANG Yujing,DANG Langfei. Video detection of foreign objects on the surface of belt conveyor underground coal mine based on improved SSD[J]. Journal of Ambient Intelligence and Humanized Computing,2020:1-10.
|
[8] |
郝帅,张旭,马旭,等. 基于CBAM−YOLOv5的煤矿输送带异物检测[J]. 煤炭学报,2022,47(11):4147-4156. DOI: 10.13225/j.cnki.jccs.2021.1644
HAO Shuai,ZHANG Xu,MA Xu,et al. Foreign object detection in coal mine conveyor belt based on CBAM-YOLOv5[J]. Journal of China Coal Society,2022,47(11):4147-4156. DOI: 10.13225/j.cnki.jccs.2021.1644
|
[9] |
程德强,徐进洋,寇旗旗,等. 融合残差信息轻量级网络的运煤皮带异物分类[J]. 煤炭学报,2022,47(3):1361-1369. DOI: 10.13225/j.cnki.jccs.xr21.1736
CHENG Deqiang,XU Jinyang,KOU Qiqi,et al. Lightweight network based on residual information for foreign body classification on coal conveyor belt[J]. Journal of China Coal Society,2022,47(3):1361-1369. DOI: 10.13225/j.cnki.jccs.xr21.1736
|
[10] |
XIAO Dong,KANG Zhuang,YU Hang,et al. Research on belt foreign body detection method based on deep learning[J]. Transactions of the Institute of Measurement and Control,2022,44(15):2919-2927. DOI: 10.1177/01423312221094393
|
[11] |
WANG C Y, BOCHKOVSKIY A, LIAO H. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J/OL]. [2022-09-28]. https://arxiv.org/abs/2207.02696.
|
[12] |
杨骥,杨亚东,梅雪,等. 基于改进的限制对比度自适应直方图的视频快速去雾算法[J]. 计算机工程与设计,2015,36(1):221-226. DOI: 10.16208/j.issn1000-7024.2015.01.040
YANG Ji,YANG Yadong,MEI Xue,et al. Fast video dehazing based on improved contrast limited adaptive histogram equalization[J]. Computer Engineering and Design,2015,36(1):221-226. DOI: 10.16208/j.issn1000-7024.2015.01.040
|
[13] |
舒甜督. 医学CT图像的增强与分类算法研究[D]. 长春: 长春工业大学, 2022.
SHU Tiandu. Research on enhancement and classification algorithm of medical CT images[D]. Changchun: Changchun University of Technology, 2022.
|
[14] |
QIN Xiaoyi, LI Na, WENG Chao, et al. Simple attention module based speaker verification with iterative noisy label detection[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, 2021.
|
[15] |
CHOLLET F. Xception: Deep learning with depthwise separable convolutions[C]. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017.
|
[16] |
顾德英,罗聿伦,李文超. 基于改进YOLOv5算法的复杂场景交通目标检测[J]. 东北大学学报(自然科学版),2022,43(8):1073-1079.
GU Deying,LUO Yulun,LI Wenchao. Traffic target detection in complex scenes based on improved YOLOv5 algorithm[J]. Journal of Northeastern University(Natural Science),2022,43(8):1073-1079.
|
[17] |
MAO Qinghua,WANG Yufei,ZHANG Xuhui,et al. Clarity method of fog and dust image in fully mechanized mining face[J]. Machine Vision and Applications,2022,33(2):1-16.
|
[18] |
LI Kexin,QIN Liang,LI Qiang,et al. Improved edge lightweight YOLOv4 and its application in on-site power system work[J]. Global Energy Interconnection,2022,5(2):168-180. DOI: 10.1016/j.gloei.2022.04.014
|
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