基于多任务深度卷积神经网络的显著性对象检测算法

摘要

针对当前基于深度学习的显著性对象检测算法不能准确保存对象边缘的区域,从而导致检测出的显著性对象边缘区域模糊、准确率不高的问题,提出了一种基于多任务深度学习模型的显著性对象检测算法.首先,基于深度卷积神经网络(CNN),训练一个多任务模型分别学习显著性对象的区域和边缘的特征;然后,利用检测到的边缘生成大量候选区域,再结合显著性区域检测的结果对候选区域进行排序和计算权值;最后提取出完整的显著性图.在三个常用标准数据集上的实验结果表明,所提方法获得了更高的准确率,其中F-measure比基于深度学习的算法平均提高了1.9％,而平均绝对误差(MAE)平均降低了12.6％.%The current deep learning-based salient object detection algorithms fail to produce accurate object boundaries,which makes the regions along object contours blurred and inaccurate.To solve the problem,a salient object detection algorithm based on multi-task deep learning model was proposed.Firstly,based on deep Convolutional Neural Network (CNN),a multi-task model was used to separately learn region and boundary features of a salient object.Secondly,the detected object boundaries were utilized to produce a number of region candidates.After that the region candidates were reranked and their weights were computed by combining the results of salient region detection.Finally,the entire saliency map was extracted.The experimental results on three widely-used benchmarks show that the proposed method achieves better accuracy.According to F-measure,the proposed method averagely outperforms the deep learning-based algorithm by 1.9％,while lowers the Mean Absolutely Error (MAE) by 12.6％.