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An Enhanced Insect Pest Counter Based on Saliency Map and Improved Non-Maximum Suppression.

Qingwen Guo1, Chuntao Wang1,2, Deqin Xiao1

  • 1College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.

Insects
|August 27, 2021
PubMed
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This study introduces a novel method for insect pest counting on yellow sticky traps using saliency maps and improved non-maximum suppression. The approach enhances accuracy in monitoring agricultural pests from digital images.

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate insect pest counting from digital images on yellow sticky traps is crucial for effective pest monitoring.
  • Existing methods face challenges with small objects and simple backgrounds typical of yellow sticky trap imagery.

Purpose of the Study:

  • To develop an improved automated system for counting insect pests from digital images.
  • To enhance the accuracy and reliability of insect pest detection and quantification in agricultural settings.

Main Methods:

  • A saliency map-based region proposal generator was developed, incorporating saliency map building, activation region formation, and background-foreground classification.
  • A convolutional neural network (CNN) model was employed for classifying proposed regions and generating detection bounding boxes.
Keywords:
deep learninginsect pest countingnon-maximum suppressionsaliency maptune-up box

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  • An improved non-maximum suppression technique was introduced to refine redundant bounding boxes, followed by counting for pest quantification.
  • A dual-path network integrating Faster R-CNN with the proposed counter was constructed to address challenges with closely positioned pests.

    • Main Results:

    • The proposed insect pest counters demonstrated significant improvements in F1 score compared to state-of-the-art object detectors.
    • The methods achieved higher accuracy in detecting and counting insect pests, even when individuals were close together.
    • The dual-path network effectively handled complex scenarios, leading to more reliable pest counts.

    Conclusions:

    • The developed saliency map and improved non-maximum suppression approach offers a robust solution for automated insect pest counting.
    • The integration with Faster R-CNN further boosts performance, providing a valuable tool for precision agriculture and pest management.
    • This work advances automated image analysis techniques for ecological and agricultural monitoring applications.