【摘要】 为提高复杂环境下无人机获取的作物杂草图像识别的准确率，提出一种基于图像增强与注意力机制的作物杂草识别方法。在多尺度Retinex算法中加入颜色恢复函数调节3个通道颜色的占比以恢复其颜色特征，使图像更清晰；将残差网络模型中的激活函数换为Leaky ReLU,加入CBAM注意力机制模块，获取更多有用信息，抑制其它无用信息。实验结果表明，该方法可以提高复杂环境下无人机获取的作物杂草图像的识别准确率，其准确率达到95.3%,高于AlexNet、ResNet18、ResNet50及其它主流算法的识别结果。更多还原

【Abstract】 To improve the accuracy of crop weed image recognition obtained using UAV(unmanned air vehicle) in complex environment, a crop weed recognition method based on image enhancement and attention mechanism was proposed. In the multi-scale Retinex algorithm, a color restoration function was added to adjust the proportion of the colors of the three channels to restore their color features and make the image clearer. The activation function in the residual network model was replaced by Leaky ReLU, and the CBAM attention mechanism module was added to obtain more useful information and suppress other useless information. Experimental results show that the proposed method can improve the recognition accuracy of crop weed images obtained using UAV in complex environment, and its accuracy reaches 95.3%, which is higher than the recognition results of AlexNet, ResNet18, ResNet50 and other mainstream algorithms.更多还原