Detection of coal gangue based on spectral technology and enhanced lightweight YOLOv7-tiny-Reference-Cited by-同舟云学术

Detection of coal gangue based on spectral technology and enhanced lightweight YOLOv7-tiny

Published:2024-01-07 Issue: Volume: Page:1-21
ISSN:1939-2699
Container-title:International Journal of Coal Preparation and Utilization
language:en
Short-container-title:International Journal of Coal Preparation and Utilization

Author:

Yan Pengcheng¹²³,Wang Wenchang²^ORCID,Li Guodong²,Zhao Yuting²,Wang Jingbao²,Wen Ziming²

Affiliation:

1. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan, China

2. School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China

3. Collaborative Innovation Center of Mine Intelligent Equipment and Technology, Anhui University of Science and Technology, Huainan, China

Funder

National Key Research and Development Program of China under Grant

Anhui Provincial Postdoctoral Research Funding Programs under Grant

Open Research Grant of Collaborative Innovation Center of Mine Intelligent Equipment and Technology

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology,Fuel Technology,General Chemical Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/19392699.2023.2301314

Reference26 articles.

1. Analyzing the Performance of Multilayer Neural Networks for Object Recognition

2. Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview

3. SSD: Single Shot MultiBox Detector

4. Development of Air Dense Medium Fluidized Bed Technology For Dry Beneficiation of Coal – A Review

5. Bochkovskiy, A., C. Y. Wang, and H. Liao. 2023. “YOLOv7: Trainable Bag-Of-Freebies Sets New State-Of-The-Art for Real-Time Object Detectors.” 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 7464–7475. https://doi.org/10.1109/CVPR52729.2023.00721.