# Rice pest detection via multi-scale edge network and wavelet attention enhancement

## TL;DR

This paper introduces BEAM-YOLO, a new method for detecting rice pests using advanced computer vision techniques to improve accuracy and efficiency in agriculture.

## Contribution

The novel BEAM-YOLO framework with four new modules improves pest detection accuracy and efficiency in complex field conditions.

## Key findings

- BEAM-YOLO achieves 86.6±0.5% mAP@50 and 72.7±0.9% mAP@50-95 for rice pest detection.
- It outperforms YOLOv11 by 3.3% and 3.0% in mAP@50 and mAP@50-95 respectively.
- The JRICE-PD dataset includes 4,565 images of 11 economically significant rice pests.

## Abstract

Rice pest detection faces critical challenges including small target recognition difficulties, high morphological similarities, and complex field backgrounds. This study proposes BEAM-YOLO (Bi-branch Edge Attention Multi-scale YOLO) to address these limitations.We constructed the JRICE-PD dataset encompassing 11 economically significant rice pests (4,565 images) and developed four innovative modules: a Multi-scale morphological Edge Network (MEN) enhancing feature discrimination; a Bi-branch Attention Feature Enhancement (BAFE) module utilizing Haar wavelet transform for foreground-background separation; an Enhanced Multi-scale Bidirectional Feature Pyramid Network (EM-BFPN) optimizing information interaction; and a Spatial-Channel Augmented Upsampling (SCAU) improving small target detection.BEAM-YOLO achieves 86.6±0.5% mAP@50 and 72.7±0.9% mAP@50-95, outperforming YOLOv11 by 3.3% and 3.0% respectively, while maintaining relatively low computational overhead and parameter count. This research provides reliable algorithmic support for intelligent agricultural pest monitoring systems, contributing to precision agriculture advancement and application.