Salient target detection method based on attention mechanism and multi-scale feature fusion

The invention discloses a saliency target detection method based on an attention mechanism and multi-scale feature fusion, and the method comprises the steps: S1, carrying out the data preprocessing, and constructing a data set and labels needed by the training and testing of a deep learning model; s2, constructing a saliency target detection network based on an attention mechanism and multi-scale feature fusion; s3, inputting the training data set in the S1 into the saliency target detection network constructed in the S2 for training to obtain a saliency target detection model; and S4, randomly selecting pictures and inputting the pictures into the model to obtain a detection result. According to the method, the features of the salient region are effectively extracted, and redundant features are effectively filtered. 本发明公开了一种基于注意力机制和多尺度特征融合的显著性目标检测方法，包括：S1、进行数据预处理，构建深度学习模型训练及测试所需的数据集及标签；S2、构建基于注意力机制和多尺度特征融合的显著性目标检测网络；S3、将S1中的训练数据集输入到S2中构建的显著性目标检测网络中进行训练，得到显著性目标检测模型；S4、随机挑选图片输入到该模型中，得到检测结果。根据本发明，有效的提取到显著性区域的特