关键词: 农田杂草, 注意力机制, 强化注意力, 位置特征编码, 迁移学习

Abstract: Aiming at the problem that it is difficult to recognize weeds and other targets due to the cross growth between crops and weeds, an agricultural weeds recognition method based on enhanced attention mechanism is proposed. Firstly, the backbone network is used to extract the deep feature of the input images, and on this basis, an enhanced attention module is proposed, which is used to encode the position feature from the horizontal and vertical dimensions, and then strengthening the position and recognition of the target object by calculating the offset between the feature extracted from the original backbone network and the position encode. Then, the singlelayer based on attention mechanism, the context chain is introduced to further strengthen the generalization performance of the model. Finally, the training method combined with the transfer learning can alleviate the problem of the over fitting caused by small sample data sets. By testing the recognition performance of the single target object and cross growing multitarget objects in multi scene environments such as sunny, rainy and cloudy days, the results show that the proposed method can achieve the average recognition accuracy of 92.84% and 90.01% for the single target object and cross growing multitarget objects respectively.

Key words: agricultural weeds, attention mechanism, strengthen attention, position feature encode, transfer learning