Abstract: The performance of salient object detection is greatly improved by the superior feature extraction ability of Fully Convolutional Neural Networks(FCN).However，the simple fusion strategies (feature addition or concatenation) cannot effectively enhance features，resulting in algorithms object misdetection and missed detection in complex scenes.The paper proposed a specifically feature enhancement method to improve the performance of salient object detection.Firstly，object misdetection mostly occurs in a scene where the background is cluttered or the object and the background are intertwined，so it greatly alleviate the object misdetection problem from the perspective of global enhancement and structural enhancement，respectively.Secondly，the missed detection of the object generally occurs in the interior and edge of the object，so the study introduce residual learning to learn the information of the missed region and refine the loss of the object interior and edge.Finally，comparison results between the proposed method with other 13 kinds of advanced methods over 5 benchmark datasets indicate that the proposed model is superior to other 13 methods，and the problems of object misdetection and missed detection in complex scenes were successfully solved.

Key words: fully convolutional neural networks, salient object detection, feature enhancement, object misdetection, missed detection