Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features
Source abstract
Abstract
To solve the missed and wrong detection problems of the object detection model in identifying soybean companion weeds, this paper proposes an enhanced multi-scale channel feature model based on RT-DETR (EMCF-RTDETR). First, we designed a lightweight hybrid-channel feature extraction backbone network, which consists of a CGF-Block module and a FasterNet-Block module working together, aiming to reduce the amount of computation and the number of parameters while improving the efficiency of feature extraction. Second, we constructed the EA-AIFI module. This module enhances the extraction of detailed features by combining the in-scale feature interaction module with the Efficient Additive attention mechanism. In addition, we designed an Enhanced Multiscale Feature Fusion (EMFF) network structure, which first differentiates the inputs of the three feature layers and then ensures the effective flow between the original and enhanced features of each feature layer by two multiscale feature fusions as well as one diffusion. The experimental results demonstrate that the EMCF-RTDETR model improves the average precision mAP50 and mAP50:95 by 3.3% and 2.2%, respectively, compared to the RT-DETR model, and the FPS is improved by 10%. Moreover, our model outperforms other mainstream detection models in terms of accuracy and speed, revealing its significant potential for soybean weed detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Spectroscopy and Chemometric Analyses · Advanced Chemical Sensor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, China
BiSiCloud (Wuhan) Information Technology Co., Ltd., Wuhan 430024, China
School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China
Zhengzhou Xinsiqi Technology Co., Ltd., Zhengzhou 450046, China
Funding
National Natural Science Foundation of China · Humanities and Social Science Research Foundation of Ministry of Education of China · Ministry of Education Industry-University Cooperation Education Project · 2025 ESI (Engineering) · Hubei Provincial Natural Science Foundation · School Enterprise Cooperation Project
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