articleJun 1, 2019Closed access

IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition

Nankai University · Cardiff University

Indexed incrossref

Abstract

Insect pests are one of the main factors affecting agricultural product yield. Accurate recognition of insect pests facilitates timely preventive measures to avoid economic losses. However, the existing datasets for the visual classification task mainly focus on common objects, e.g., flowers and dogs. This limits the application of powerful deep learning technology on specific domains like the agricultural field. In this paper, we collect a large-scale dataset named IP102 for insect pest recognition. Specifically, it contains more than 75, 000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. In addition, we annotate about 19, 000 images with bounding boxes for object…

Citation impact

490
total citations
FWCI
27.91
Percentile
100%
References
56
Citations per year

Authors

5

Topics & keywords

Keywords
  • Computer science
  • Artificial intelligence
  • Machine learning
  • Benchmark (surveying)
  • Feature extraction
  • Insect pest
  • Object detection
  • Pattern recognition (psychology)
UN Sustainable Development Goals
  • Zero hunger
No related works found for this paper.