DC-YOLO: an improved field plant detection algorithm based on YOLOv7-tiny

Abstract

Abstract Weeding is an important part of agricultural production. With the development of science and technology, automated weeding is regarded as the future development direction, and how to accurately and efficiently detect plants in the field is one of the key points. Corn seedlings and weeds are similar in color, shape and other characteristics, which brings serious challenges to plant detection. In this paper, we propose an improved model based on YOLOv7-tiny, called DC-YOLO. To improve the extraction of key features in the model, we propose Dual Coordinate Attention model (DCA). In addition, we introduce the Content-Aware ReAssembly of FEatures (CARAFE) operator to represent the up-sampling process as a learnable feature reorganization, which enriches the feature information of the sampled images. Finally, we decoupled the detection head to minimize conflicts between features from different tasks. The results show that applying the proposed method to corn and weed datasets, the detection accuracy of the model reaches 95.7% mean Average Precision ([email protected]), the computational effort of the model is 13.083 Giga Floating-point Operations (GFLOPs), and the parameter size is 5.223 Millon (M), which is better than the rest of the mainstream light-weight target detection model.