Reports on Electronics Findings from Xiamen University of Technology Provide New Insights (Instance Segmentation of Irregular Deformable Objects for Power Operation Monitoring Based on Multi-Instance Relation Weighting Module)

2023 MAY 23 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- New study results on electronics have been published. According to news originating from Xiamen, People’s Republic of China, by NewsRx correspondents, research stated, “Electric power operation is necessary for the development of power grid companies, where the safety monitoring of electric power operation is difficult.”

Financial supporters for this research include The National Natural Science Foundation of China; The Natural Science Foundation of The Department of Science And Technology of Fujian Province; The Foundation For Science And Technology Cooperation Program of Longyan.

The news journalists obtained a quote from the research from Xiamen University of Technology: “Irregular deformable objects commonly used in electrical construction, such as safety belts and seines, have a dynamic geometric appearance which leads to the poor performance of traditional detection methods. This paper proposes an end-to-end instance segmentation method using the multi-instance relation weighting module for irregular deformable objects. To solve the problem of introducing redundant background information when using the horizontal rectangular box detector, the Mask Scoring R-CNN is used to perform pixel-level instance segmentation so that the bounding box can accurately surround the irregular objects. Considering that deformable objects in power operation workplaces often appear with construction personnel and the objects have an apparent correlation, a multi-instance relation weighting module is proposed to fuse the appearance features and geometric features of objects so that the relation features between objects are learned end-to-end to improve the segmentation effect of irregular objects. The segmentation mAP on the self-built dataset of irregular deformable objects for electric power operation workplaces reached up to 44.8%. With the same 100,000 training rounds, the bounding box mAP and segmentation mAP improved by 1.2% and 0.2%, respectively, compared with the MS R-CNN.”

According to the news reporters, the research concluded: “Finally, in order to further verify the generalization performance and practicability of the proposed method, an intelligent monitoring system for the power operation scenes is designed to realize the actual deployment and application of the proposed method. Various tests show that the proposed method can segment irregular deformable objects well.”

For more information on this research see: Instance Segmentation of Irregular Deformable Objects for Power Operation Monitoring Based on Multi-Instance Relation Weighting Module. Electronics, 2023,12(9). (Electronics - http://www.mdpi.com/journal/electronics). The publisher for Electronics is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/electronics12092126.

Our news editors report that additional information may be obtained by contacting Weihao Chen, School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361024, People’s Republic of China. Additional authors for this research include Lumei Su, Zhiwei Lin, Xinqiang Chen, Tianyou Li.

ORCID is an identifier for authors and includes bibliographic information. The following is ORCID information for the author of this research: Weihao Chen (http://orcid.org/0009-0009-4541-1069).

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