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Related Experiment Video

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Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

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Efficient Optimized YOLOv8 Model with Extended Vision.

Qi Zhou^1,2, Zhou Wang^1,2, Yiwen Zhong^1,2

¹College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.

Sensors (Basel, Switzerland)

|October 26, 2024

Summary

This study introduces YOLO-EV, an enhanced YOLOv8 object detection model. YOLO-EV improves feature extraction and localization accuracy, demonstrating superior performance in complex agricultural weed detection scenarios.

Keywords:

YOLOv8 attention mechanism complex environments efficient computing object detection

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Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

Object detection algorithms face challenges in complex scenarios.
Enhancing algorithm performance is crucial for real-world applications.

Purpose of the Study:

To present an efficient, optimized YOLOv8 model with extended vision (YOLO-EV).
To improve object detection performance, especially in complex environments like agricultural weed identification.

Main Methods:

Integration of a multi-branch group-enhanced fusion attention (MGEFA) module for feature extraction.
Enhancement of the spatial pyramid pooling fast (SPPF) layer with large scale kernel attention (LSKA).
Replacement of IOU loss with Wise-IOU loss and addition of a P6 layer for improved multi-scale detection and localization accuracy.

Main Results:

YOLO-EV demonstrates higher computational efficiency than YOLOv8s.
Preliminary tests on VOC12 show effectiveness in standard object detection.
Superior detection accuracy on complex CottonWeedDet12 and CropWeed datasets compared to YOLOv8s and other state-of-the-art models.

Conclusions:

YOLO-EV offers significant practical application potential for complex object detection tasks.
The model effectively identifies and locates various weed types in challenging agricultural scenes.
The proposed enhancements lead to improved accuracy and efficiency in object detection.