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Related Experiment Video

Updated: Aug 12, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Framework for environment perception: Ensemble method for vision-based scene understanding algorithms in agriculture.

Esma Mujkic^1,2, Ole Ravn¹, Martin Peter Christiansen²

¹Automation and Control Group, Department of Electrical and Photonics Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.

Frontiers in Robotics and AI

|January 30, 2023

Summary

This study introduces an ensemble method for agricultural autonomous vehicles, combining object detection and anomaly detection to improve environment perception and increase detected objects in field images.

Keywords:

anomaly detection ensemble models environment perception object detection semantic segmentation

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Area of Science:

Agricultural Engineering
Computer Vision
Robotics

Background:

Autonomous agricultural vehicles require robust environment perception for safe operation.
Vision-based algorithms are crucial for detecting objects and structures in agricultural fields.

Purpose of the Study:

To develop an ensemble method for combining semantic segmentation, object detection, and anomaly detection for agricultural scene understanding.
To enhance the reliability of environment perception systems for autonomous agricultural vehicles.

Main Methods:

An ensemble framework was proposed, integrating an object detector for agriculture-specific classes and an anomaly detector for other objects.
Semantic segmentation maps were used to determine if detected objects were within the field area.
The ensemble method combined outputs from different algorithms at inference time, ensuring algorithm independence.

Main Results:

The proposed ensemble method successfully integrated outputs from object detection, anomaly detection, and semantic segmentation.
Combining object detection with anomaly detection significantly increased the number of detected objects in agricultural images.
The framework demonstrated improved environment perception capabilities for autonomous vehicles.

Conclusions:

The developed ensemble method enhances the performance of environment perception systems in agriculture.
This approach offers a flexible and effective way to improve object detection and scene understanding for autonomous agricultural vehicles.