# GEFA-YOLO: Lightweight Weed Detection with Group-Enhanced Fusion Attention

## TL;DR

This paper introduces a new weed detection model for cotton fields that is efficient and accurate, suitable for use on edge devices in agriculture.

## Contribution

The novel grouped enhanced fusion attention (GEFA) mechanism improves feature expression while reducing computational costs.

## Key findings

- GEFA achieves a good balance of efficiency, accuracy, and complexity on multiple datasets.
- The model has minimal parameter and computational cost increases but significantly improves accuracy.
- The system is practical for edge devices and real-time camera detection in agriculture.

## Abstract

Cotton is an important economic crop, and its weed management directly affects yield and quality. In actual cotton fields, detection accuracy still faces challenges due to the complex types of weeds, variable morphologies, and environmental factors. Most existing models rely on the attention mechanism to improve performance, but channel attention tends to ignore spatial information, while full spatial attention brings high computational costs. Therefore, this paper proposes a grouped enhanced fusion attention mechanism (GEFA), which combines grouped convolution and local spatial attention to reduce complexity and parameter quantity while effectively enhancing feature expression ability. The GEFAY detection model constructed based on GEFA achieves good balance in efficiency, accuracy, and complexity on the CottonWeedDet12, VOC, and COCO datasets. Compared with classic attention methods, this model has the smallest increase in parameters and computational costs while significantly improving accuracy. It is more suitable for deployment on edge devices. The further designed end-to-end intelligent weed detection system and edge device deployment can achieve image detection on local maps and real-time cameras, with good practicality and scalability, providing effective technical support for intelligent visual applications in precision agriculture.