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Precise Crop Pest Detection Based on Co-Ordinate-Attention-Based Feature Pyramid Module.

Chenrui Kang^1,2, Lin Jiao^2,3, Kang Liu⁴

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

|January 25, 2025

Summary

This summary is machine-generated.

Deep learning models struggle with small insect pest detection. A new co-ordinate attention-based feature pyramid network (CAFPN) improves feature extraction and sample selection for accurate pest identification.

Keywords:

co-ordinate attention crop pest feature pyramid network object detection sample selection small pest

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Insect pests significantly impact global crop production and economic value.
Accurate and rapid pest detection is essential for effective pest management and infestation mitigation.
Current deep learning methods face challenges in detecting small crop pests due to difficulties in feature extraction and sample selection.

Purpose of the Study:

To develop an advanced deep learning model for accurate detection and recognition of small-sized crop pests.
To address the limitations of existing methods in feature extraction and positive/negative sample selection for small pest detection.

Main Methods:

Designed a co-ordinate-attention-based feature pyramid network (CAFPN) for enhanced salient visual feature extraction.
Implemented a dynamic sample selection strategy with positive and negative weight functions during network training.
Evaluated the model on large-scale datasets: AgriPest 21 and IP102.

Main Results:

The CAFPN model achieved promising detection results on benchmark datasets.
Achieved mean average precision (mAP) scores of 77.2% on AgriPest 21 and 29.8% on IP102.
Demonstrated superior performance compared to other existing pest detection models.

Conclusions:

The proposed CAFPN model effectively overcomes limitations in small pest detection by improving feature extraction and sample selection.
The dynamic sample selection strategy enhances both classification accuracy and localization precision.
The results indicate a significant advancement in deep learning-based crop pest detection systems.