Mine underground object detection algorithm based on TTFNet and anchor-free

Abstract: To solve the problem of poor object detection effect caused by uneven light and high noise in underground mines, this study proposes a TTFNet (training-time-friendly network)-based object detection algorithm for underground mines. First, CenterNet and TTFNet algorithms are introduced, then pooling is introduced into CSPNet basic structure to design a lightweight feature extraction network, at the same time optimizing the feature fusion way in the original algorithm, optimizing residual shrinkage network structure, and introducing it into object detection task. Experiments were conducted on the established underground data set. The results show that compared with the original algorithm, our proposed algorithm can still maintain similar accuracy while significantly reducing model parameters; compared with other anchor-based detection algorithms, it has achieved similar overall performance.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Mine underground object detection algorithm based on TTFNet and anchor-free ; volume:14 ; number:1 ; year:2024 ; extent:12 ; Open computer science ; 14, Heft 1 (2024) (gesamt 12)

Beteiligte, Orts- und Zeitangaben

• Urheber: Song, Zhen ; Qing, Xuwen ; Zhou, Meng ; Men, Yuting

Weitere Informationen

• DOI: 10.1515/comp-2024-0015
• URN: urn:nbn:de:101:1-2411281810188.476686537846
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:32 MESZ