MSFANet: A Light Weight Object Detector Based on Context Aggregation and Attention Mechanism for Autonomous Mining Truck

Accurate and reliable object detection is a fundamental component of perception system for autonomous driving. Specially, in some circumstances like autonomous driving in surface mine, there is a fact that the particularity of scene brings tremendous challenges for object detection with a series of problems caused by the multi-scale and camouflaged objects. In this paper, a multi-scale feature fusion and attention based multi-branches framework was proposed to improve the performance of object detection for above problems called MSFANet. In the proposed MSFANet, a multi-scale feature fusion module, which was used to capture the rich context features for multi-scale high level feature maps, and a multi-scale attention module, which was used to enhance the feature saliency of objects with different scales, were designed. What's more, to improve the performance of multi-scale object detection, the authors build 4 different prediction branches for large, medium small and smaller scale objects respectively. At last, the authors built their own dataset for automatic driving in surface mine called SurMine and test the model at their own datasets and KITTI benchmark. It achieved 82.7 mAP(%) and 92.57 mAP(%) in 32 36 ms on a TITAN RTX, compared to 80.2 mAP(%) and 87.83 mAP(%) in 28 ~ 34 ms by YOLOv7 on SurMine and KITTI benchmarks.

• Record URL:
  http://dx.doi.org/10.1109/TIV.2022.3221767
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Song, Ruiqi
  • Ai, Yunfeng
  • Tian, Bin
  • Chen, Long
  • Zhu, Fenghua
  • Yao, Fei
• Publication Date: 2023-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 2285-2295
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 8
  • Issue Number: 3
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Detectors; Digital mapping; Mining; Proximity detectors; Real time control
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01900081
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 20 2023 9:12AM