Comparison of Object Detection Algorithms on Maritime Vessels

Abstract

This manuscript conducts a comparison on modern object detection systems in their ability to detect multiple maritime vessel classes. Three highly scoring algorithms from the Pascal VOC Challenge, Histogram of Oriented Gradients by Dalal and Triggs, Exemplar-SVM by Malisiewicz, and Latent-SVM with Deformable Part Models by Felzenszwalb, were compared to determine performance of recognition within a specific category rather than the general classes from the original challenge. In all cases, the histogram of oriented edges was used as the feature set and support vector machines were used for classification. A summary and comparison of the learning algorithms is presented and a new image corpus of maritime vessels was collected. Precision-recall results show improved recognition performance is achieved when accounting for vessel pose. In particular, the deformable part model has the best performance when considering the various components of a maritime vessel.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
ADA619022

Entities

People

  • Brendan Morris
  • Bryan Auslander
  • David W. Aha
  • Kalyan Gupta
  • Mark Chua

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computer Vision
  • Computing-Related Activities
  • Detection
  • Histograms
  • Learning
  • Machine Learning
  • Military Research
  • Ocean Waves
  • Orientation (Direction)
  • Precision
  • Test Sets
  • Training
  • Vascular System Injuries
  • Water Waves
  • Waves

Readers

  • Computer Vision.
  • Oceanography.

Technology Areas

  • AI & ML