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Related Experiment Video

Updated: Mar 29, 2026

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Published on: February 2, 2019

Enhanced Real-Time Detector for Industrial Vision-Based Corn Impurity Detection.

Xiao Zhang¹, Yuhang Bian¹, Xiangdong Li¹

¹Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Engineering, China Agricultural University, 17 Qinghua Donglu, P.O. Box 50, Beijing 100083, China.

Foods (Basel, Switzerland)

|March 28, 2026

Summary

This study introduces an enhanced real-time detector transformer model for corn impurity detection, improving accuracy and speed. The RT-DETR-CD model effectively handles occlusions and diverse impurities in industrial vision applications.

Keywords:

RT-DETR corn cleaning grain quality

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TD-DFT Guided Advanced E-Eye Sensing Technique for On-site Quantification of Fe, Cr, F, and As in the Environmental, Biological, and Food Samples

TD-DFT Guided Advanced E-Eye Sensing Technique for On-site Quantification of Fe, Cr, F, and As in the Environmental, Biological, and Food Samples

Published on: September 19, 2025

Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

Effective corn cleaning is vital for grain quality and safety.
Traditional Convolutional Neural Network (CNN) methods face challenges with occlusions and diverse impurity types.
Existing methods lack generalization and struggle with image quality limitations.

Purpose of the Study:

To propose an enhanced real-time detector transformer model for corn impurity detection.
To improve accuracy and speed in detecting impurities under challenging industrial conditions.
To address limitations in traditional CNN-based detection methods.

Main Methods:

Developed RT-DETR-CD (Real-Time Detector Transformer with Convolution and Dynamic Upsampling) model.
Integrated Receptive Field Attention Convolutions (RFAConv) for enhanced local texture sensitivity.
Employed dynamic upsampling (DySample) for high-frequency edge restoration and Inner-Shape-IoU loss for improved bounding box regression.

Main Results:

The proposed RT-DETR-CD model achieved a 4.7% improvement in mean average precision (mAP).
The model operates at a high speed of 68 frames per second (FPS).
Demonstrated superior performance in accuracy and speed compared to the original RT-DETR model.

Conclusions:

The RT-DETR-CD model offers a practical solution for real-time, high-precision impurity detection in grain processing.
The integration of RFAConv, DySample, and Inner-Shape-IoU loss enhances detection capabilities.
This approach provides a significant advancement for industrial vision applications in agriculture.