收藏切换
A Coke Intelligent Loading Overflow Detection Method Based on Dark Channel Prior Knowledge and ResNet Network
收藏切换
PDF
Kang-zhan XIE1, Hui-fang HOU2, *, Zi-hao ZHANG2, Wen-tao SUN1
Science Technology and Engineering | 2025, 25(8) : 3325 - 3332
Less
收藏切换
Science Technology and Engineering | 2025, 25(8): 3325-3332
Automation and Computational Technology
A Coke Intelligent Loading Overflow Detection Method Based on Dark Channel Prior Knowledge and ResNet Network
Full
Kang-zhan XIE1, Hui-fang HOU2, *, Zi-hao ZHANG2, Wen-tao SUN1
Affiliations
  • 1 School of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China
  • 2 School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou 450001 China
Published: 2025-03-18 doi: 10.12404/j.issn.1671-1815.2402154
Outline
收藏切换

The accurate detection of coke overflow in high-dust environments is a pivotal challenge in achieving intelligent coke loading. A method was proposed to address this issue for the intelligent detection of coke loading overflow, which was based on dark channel prior knowledge and the ResNet network. Firstly, a video collector was used to obtain video information of the coke loading scene, and the original time-series video image frames were processed to obtain the region of interest between the discharge port and the loader. Secondly, the prior knowledge method of dark channels was employed to process the regions of interest. Enhancing the contrast between the target areas and irrelevant areas within the regions of interest, thereby mitigating the effects of dust on subsequent detection models. Moreover, the problem of overflow detection was transformed into a binary classification task by labeling the regions of interest based on the actual loading of coke. Finally, the ResNet network was utilized for modeling, enabling the completion of model training and experimentation during the loading process of newly acquired coke. The experimental results demonstrate that the proposed method exhibits promising performance on new data, achieving an overall accuracy of 86.81%. Specifically, the accuracy, recall, and F1 score for the overflow class are 84. 12%, 90.74%, and 0.8730, respectively. Furthermore, the application of the dark channel prior algorithm in data processing results in a notable increase in the recall rate of the overflow class by 3.31%.

coke intelligent loading overflow detection  /  prior knowledge of dark channels  /  ResNet network
Kang-zhan XIE, Hui-fang HOU, Zi-hao ZHANG, Wen-tao SUN. A Coke Intelligent Loading Overflow Detection Method Based on Dark Channel Prior Knowledge and ResNet Network[J]. Science Technology and Engineering, 2025 , 25 (8) : 3325 -3332 . DOI: 10.12404/j.issn.1671-1815.2402154
Year 2025 volume 25 Issue 8
PDF
260
103
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2402154
  • Receive Date:2024-03-26
  • Online Date:2025-07-29
  • Published:2025-03-18
Article Data
Affiliations
History
  • Received:2024-03-26
  • Revised:2024-12-16
Funding
Affiliations
    1 School of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China
    2 School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou 450001 China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2402154
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT