Substation instrumentation target detection based on multi‐scale feature fusion. (13th July 2022)

• Record Type: Journal Article
• Authors: Feng, Qiaosheng; Huang, Li; Sun, Ying; Tong, Xiliang; Liu, Xin; Xie, Yuanmin; Li, Jun; Fan, Hanwen; Chen, Baojia
• Is Part Of: Concurrency and computation. Volume 34:Number 23(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-13
• Subjects: inspection robot -- instrument target detection -- multi‐scale feature fusion -- SSD; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7177
• Languages: English
• ISSNs: 1532-0626

Abstract

SUMMARY: With the promotion of smart grid construction work, the use of high‐precision and high‐efficiency substation inspection robot has become the development trend of substation inspection. A multi‐scale feature fusion meter target detection algorithm is proposed to address the problems of low efficiency and susceptibility to surrounding environmental factors by the traditional manual meter reading method. Kinecct is used to acquire color images of substation meters with different backgrounds, light intensities, and angles to build a substation meter dataset. Based on the complementarity and correlation of multi‐scale features, an SSD target detection model with multi‐scale feature fusion is established, and the performance of the algorithm is tested on the constructed dataset, and comparative experiments are conducted to verify the effectiveness of the algorithm for target detection accuracy improvement.