An underground coal mine multi-target detection algorithm
Currently, underground coal mine target detection algorithms based on deep learning show poor performance in detecting complex small targets under conditions of uneven light intensity distribution, complex target environments, and imbalanced multi-class target scale distribution, often resulting in...
Saved in:
Main Authors: | FAN Shoujun, CHEN Xilin, WEI Liangyue, WANG Qingyu, ZHANG Shiyuan, DONG Fei, LEI Shaohua |
---|---|
Format: | Article |
Language: | zho |
Published: |
Editorial Department of Industry and Mine Automation
2024-12-01
|
Series: | Gong-kuang zidonghua |
Subjects: | |
Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024090035 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusion
by: Yang Guo, et al.
Published: (2025-01-01) -
Small target detection in UAV view based on improved YOLOv8 algorithm
by: Xiaoli Zhang, et al.
Published: (2025-01-01) -
Intelligent recognition algorithm and application of coal mine overhead passenger device based on multiscale feature fusion
by: Beijing XIE, et al.
Published: (2024-12-01) -
An Efficient Group Convolution and Feature Fusion Method for Weed Detection
by: Chaowen Chen, et al.
Published: (2024-12-01) -
SS-YOLO: A Lightweight Deep Learning Model Focused on Side-Scan Sonar Target Detection
by: Na Yang, et al.
Published: (2025-01-01)