【摘要】 针对田间苋菜识别存在准确率低、样本数量少等问题，通过引入扩展感受野和提取上下文信息的ASPP注意力机制改进YOLO v5苋菜识别模型，在低数据集下改进后的模型能够显著提高F1值和mAP。实验结果表明，在低数据集下引入ASPP注意力机制后苋菜识别模型F1值提高13个百分点、mAP提高18.6个百分点。采用横向录制的方式苋菜被检测到的概率提高15.4个百分点。因此，本研究为苋菜或其他杂草在低数据集下的识别提供了有效的方法，为农业领域的杂草识别和管理研究提供了参考。更多还原

【Abstract】 Aiming at the problems of low accuracy and small number of samples in field amaranth identification, the YOLO v5 amaranth identification model was improved by introducing ASPP attention mechanism of expanding receptive field and extracting context information. The improved model would significantly improve F1 value and mAP index under low data set. The experimental results showed that the F1 value and mAP of amaranth identification model was increased by 13 percentage points and 18.6 percentage points after the introduction of ASPP attention mechanism in low data set. The detection rate of amaranth was increased by 15.4 percentage points with horizontal recording. Therefore, the research provided an effective method for the identification of amaranth or other weeds under low data sets, and prepared for the research of weed identification and management in the agricultural field.更多还原