Saliency Object Detection Combining Multi-Level Supervision and Boundary Loss
Multiple downsampling operations in PoolNet network can easily reduce spatial resolution,and using only binary cross entropy loss function(BCE)is not conducive to capturing salient target edge features,which can easily lead to low edge detection accuracy.In this paper,the multi-level supervision model(MSM)is added after the five-level feature fusion module(fuse)of the PoolNet network decoder,and the sum of the calculated values of BCE,SSIM and IoU loss functions is used as the boundary loss function value for the output of the five-level MSM.The av-erage value of the five-level boundary loss function value is used as the final output loss value of the network,and it is learned using the random gradient descent method.Thus,a new saliency object detection method combining multi-level supervision module with boundary loss function,namely,PoolNet-D,is proposed.The comparative experimental results show that on the six commonly used salient object detection datasets,the PoolNet-D model has significantly improved in terms ofMAE and F-measure evaluation indicators.
Salient object detectionConvolutional neural networkMulti-level supervisionBoundary loss
NETL
NSTL
万方数据
