WANG M L, YANG S, ZHANG C S，et al. An improved YOLOv8n based dislocation detection algorithm for shaft rigid tank passage joint［J］.Journal of Henan Polytechnic University（Natural Science），doi:10.16186/j.cnki.1673-9787. 2023090041

doi: 10.16186/j.cnki.1673-9787.2023090041

Received：2023-09-18

Revised：2024-03-08

Online：2025-04-27

An improved YOLOv8n based dislocation detection algorithm for shaft rigid tank passage joint

WANG Manli¹, YANG Shuang¹, ZHANG Changsen¹, ZHANG Shanwen²

(1.School of Physics ＆ Electronic Information Engineering，Henan Polytechnic University，Jiaozuo 454000，Henan, China；2. School of Electronic Information, Xijing University, Xi’an 710000, Shaanxi, China)

Abstract: [Objective] As the guiding device of the lifting system, the rigid shaft channel is very important for the safe and stable operation of the lifting system. However, in the process of use, the tank passage is prone to lateral displacement and deformation; once the passage is uneven, it will cause strong vibration of the lifting system, which has a serious impact on the operation and safety of the lifting system. In order to detect the faults of the rigid tank channel joints of vertical wells in time and eliminate the hidden dangers of the operation of the hoisting system, a misalignment detection algorithm of the rigid tank channel joints of vertical wells based on the improved YOLOv8n, YOLOv8n-CSS, is proposed. [Methods] First of all, in order to suppress the interference of weak and unbalanced image feature details collected in the wellbore, The CA attention mechanism module was integrated into the YOLOv8n backbone network to integrate the location information of the two dimensions of channel and space, which helped the model pay more attention to the important parts of the input image, improve the recognition accuracy and generalization ability of the model, and improve the feature extraction ability of local important information of the YOLOv8n backbone network under fuzzy, dark, and strong light environments. Then, in order to adapt to the input images of different scales, the network can obtain more comprehensive, detailed feature information, and the SPPF module is improved to improve the performance of the network. Then, in order to reduce the degree of freedom of regression and further improve the accuracy of regression, the SIOU loss function is used to improve the prediction accuracy of the anchor frame. Finally, the offset size of the channel joint is calculated by using the non-fixed focus ranging method. [Results] The results show that compared with the YOLOv8n network, the accuracy P and recall rate R of the YOLOv8N-CSS network on the data set of shaft rigid channel joints increased by 2.4%, an increase of 15.7%, and an average accuracy increase of 10.8% from 83.8% of the YOLOv8n network to 94.6%. [Conclusion] Compared with other YOLO algorithms, the improved algorithm in this paper has significant advantages in the dislocation detection of rigid channel joints in shafts.

Key words: rigid guide; joint dislocation; YOLOv8n; CA attention mechanism; improved SPPF module

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