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Attention-Based Multiscale Feature Pyramid Network for Corn Pest Detection under Wild Environment.

Chenrui Kang^1,2, Lin Jiao^2,3, Rujing Wang²

¹School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China.

|November 10, 2022

Summary

This summary is machine-generated.

Accurate corn pest detection is crucial for preventing crop loss. A new deep learning method using convolutional neural networks (CNNs) effectively identifies and locates pests, improving agricultural monitoring.

Keywords:

attention convolution neural network corn pest detection feature pyramid network

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Flying Insect Detection and Classification with Inexpensive Sensors

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Published on: October 15, 2014

Area of Science:

Agricultural Science
Computer Vision
Artificial Intelligence

Background:

Agricultural pest outbreaks cause significant corn production losses.
Early and accurate pest detection is vital for effective damage prevention and management.

Purpose of the Study:

To develop and evaluate a novel convolutional neural network (CNN)-based method for accurate and robust corn pest detection.
To improve the early warning system for agricultural pests to mitigate crop damage.

Main Methods:

Construction of a large-scale corn pest dataset with 7741 images across 10 classes.
Implementation of a deep residual network with deformable convolution for feature extraction.
Development of an attention-based multi-scale feature pyramid network to handle multi-scale pest detection.
Integration of these modules into a two-stage detector for pest identification and localization.

Main Results:

The proposed method achieved 70.1% mean Average Precision (mAP) and 74.3% Recall.
The system operates at a speed of 17.0 frames per second (FPS), balancing accuracy and efficiency.
Experimental results demonstrate superior performance compared to existing corn pest detection methods.

Conclusions:

The developed CNN-based approach provides a robust and efficient solution for corn pest detection.
This method enhances the capability for early warning systems, aiding in the prevention of agricultural losses.
The attention-based multi-scale feature pyramid network effectively addresses the challenge of detecting pests at various scales.