关键词: 杂草检测, YOLOv5, 数据增强, 注意力机制, 回归损失函数

Abstract: With the continuous development of intelligent agricultural technology and field planting technology, automatic weeding has a broad market prospect. Regarding the effectiveness of automatic herbicide spraying, the precise and rapid identification and positioning of farmland weeds is one of the key technologies. An improved YOLOv5 algorithm to realize weed detection can improve the model generalization of the backbone network, and improve the accuracy of the prediction box by improving the box regression loss function. The experiment shows that under the complex environment of sesame crops and multiple weeds, the mean average detection accuracy of this method is 90.6%, and the average detection accuracy of weed is 90.2%, which were  higher than the YOLOv5s model by 4.7% and 2%, respectively. In the test environment of this paper, a single image detection time is 2.8 ms, enabling realtime detection. The research content can provide a reference for intelligent weeding equipment in farmland.

Key words: weeds detection, YOLOv5, data augmentation, attention mechanism, regression loss function