Salient object detection is a hot spot of current computer vision. The emergence of the convolutional neural network (CNN) greatly improves the existing detection methods. In this paper, we present 3MNet, which is based on the CNN, to make the utmost of various features of the image and utilize the contour detection task of the salient object to explicitly model the features of multi-level structures, multiple tasks and multiple channels, so as to obtain the final saliency map of the fusion of these features. Specifically, we first utilize contour detection task for auxiliary detection and then utilize use multi-layer network structure to extract multi-scale image information. Finally, we introduce a unique module into the network to model the channel information of the image. Our network has produced good results on five widely used datasets. In addition, we also conducted a series of ablation experiments to verify the effectiveness of some components in the network.

显着目标检测是当前计算机视觉的热点。卷积神经网络（CNN）的出现极大地改进了现有的检测方法。在本文中，我们提出了基于 CNN 的 3MNet，最大限度地利用图像的各种特征，利用显着对象的轮廓检测任务，对多层次结构、多任务和多任务的特征进行显式建模。通道，从而得到这些特征融合的最终显着图。具体来说，我们首先利用轮廓检测​​任务进行辅助检测，然后利用多层网络结构提取多尺度图像信息。最后，我们在网络中引入了一个独特的模块来对图像的通道信息进行建模。我们的网络在五个广泛使用的数据集上产生了良好的结果。