FEGNet: A feature enhancement and guided network for infrared object detection in underground mines

Object detection plays an important role in underground intelligent vehicles and intelligent transportation systems. Due to the uneven light in underground mining scenarios, infrared cameras are one of the typical onboard sensors for environmental perception. Although object detection has been studied for decades, it still confronts the challenge of detecting infrared objects in underground mines. The contributing factors include weak and small objects in infrared images and similar environments in mining scenarios. In this paper, a Feature Enhancement and Guided Network (FEGNet) is proposed to address these problems. Based on the characteristics of infrared images, the feature enhancement module (FEM) preserves the image details from global and local perspectives to improve the discrimination of weak and small objects. To tackle the problem of overfitting caused by similar environments, a receptive-field-guided (RFG) backbone is proposed to learn multi-scale context and spatial information. The experimental results on the underground mining (UM) dataset demonstrate that the mAP of the proposed FEGNet achieves 86.1%, which is 4.6% higher than the state-of-the-art CNN-based network YOLOv7.

• Record URL:
  • https://doi.org/10.1177/09544070231165627
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09544070
• Supplemental Notes:
  • © IMechE 2023.
• Authors:
  • Huang, Lisha
  • 0000-0002-7710-2529
  • Zhang, Xi
  • Yu, Miao
  • Yang, Songyue
  • 0000-0003-0758-7951
  • Cao, Xiao
  • Meng, Junzhou
• Publication Date: 2024-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 2292-2301
• Serial:
  • Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
  • Volume: 238
  • Issue Number: 8
  • Publisher: Sage Publications Limited
  • ISSN: 0954-4070
  • EISSN: 2041-2991
  • Serial URL: http://pid.sagepub.com/content/current

Subject/Index Terms

• TRT Terms: Automated guided vehicle systems; Infrared detectors; Intelligent transportation systems; Intelligent vehicles; Mines
• Subject Areas: Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01935109
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 24 2024 3:00PM