# Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features
Early applications of computer vision in weed detection usually require image preprocessing, such as grayscale or binarization operations, followed by feature extraction. Eventually, the weeds are classified and identified by recognition algorithms based on the features that have been extracted, such as color, leaf texture and shape. Among the main recognition algorithms are support vector machine (SVM), random forest, artificial neural network (ANN), etc. [12; 13; 14]. This type of method has relatively low requirements on the image processor, which leads to certain limitations when identifying objects in complex backgrounds. Nowadays, research on convolutional neural networks is constantly deepening, and computer vision has also moved from the field of machine learning to the field of deep learning [15; 16]. Deep learning has a wide range of applications in life and industrial production, such as grain pile temperature detection [17], air quality prediction [18], and wind power prediction [19], among others. For object detection [20], it can be classified into two categories: one is based on convolutional neural network models, and the other on the transformer [21]. In convolutional neural network models, two-stage algorithms have higher computational time and resource consumption compared to single-stage algorithms due to the need to generate candidate regions [22]. For example, the Faster R-CNN algorithm significantly improves the detection speed by integrating the two-stage network with the region proposal network (RPN) [23]. Therefore, it has been widely used in the field of weed detection [24]. For single-stage algorithms, such as YOLO series and SSD, although the inference speed is faster than the two-stage algorithms, the accuracy is relatively low. [PERSON] et al. [25] proposed a lightweight weed detection model and the experimental results showed that the computational complexity of the model was reduced while the average accuracy was improved. There is also the case of detection, as in [26; 27], of crop pests and diseases, where the average improvement in detection accuracy is not very high, despite the improvement in detection speed. The transformer is mainly used in natural language processing applications, such as machine translation, text generation, and sentiment analysis [28]. The use of transformers in the field of object detection has been proposed only in recent years. Detection Transformer (DETR) is the first transform-based object detection model, which breaks the tradition of convolutional neural network-based models [29]. Convolutional neural network-based models require anchor point generation and non-maximum suppression (NMS) [30]. However, DETR can directly regard the target detection problem as the output of an unordered sequence. By combining transform and self-attention mechanisms, the model can not only make full use of local and global features but also simplify the detection process. Although DETR has outstanding advantages, it still faces the problems of slow convergence and limited ability to detect small targets. [PERSON] et al. [31] proposed RMS-DETR for detecting weeds in rice fields, and the experimental results showed that the average accuracy was improved by 4.4% compared to the original model. [PERSON] et al. [32] combined the semi-supervised object detection (SSOD) algorithm with DETR in order to detect the growth of Chinese cabbage, and the results were shown to be an average accuracy of 74.1%. [PERSON] et al. utilized the improved DETR in order to detect rice pests and diseases with an average accuracy improvement of about 29% [33]. In order to solve the problems of inaccurate detection and large model computation of the above models for weed detection, this paper proposes a model focusing on weed detection, Enhanced Multiscale Channel Feature Model based on RT-DETR (EMCF-RTDETR). The main contributions of this paper are as follows:  
1. A lightweight convolutional gated feature extraction backbone network is constructed.