Considering the poor performance of existing co-saliency detection algorithms in multiple salient object complex scenarios

a co-saliency detection algorithm with efficient channel attention and feature fusion was proposed. Firstly

the pre-trained deep convolutional neural network was adopted to extract multi-scale features of the images

and a saliency semantic feature extraction module with edge saliency feature was designed to avoid the lack of edge information caused by fully convolutional neural networks. Secondly

the association information between images in the group was obtained based on the inner product principle

and adaptive weighting was carried out according to the association degree; a collaborative feature extraction algorithm was designed in combination with the attention layer of efficient channel. Finally

a feature fusion module based on efficient attention layer was designed

so as to fuse the results of co-saliency feature extraction at high-level semantic features with low-level features

and supervise the predictions with multi-branches simultaneously. Three classic datasets were tested

and six existing collaborative saliency detection algorithms were compared with the proposed algorithm. Results show that the proposed algorithm not only improved the accuracy of collaborative saliency detection and the richness of edge information in complex scenarios

but also had better performance of collaborative saliency detection. The effectiveness and necessity of each designed module were further verified by ablation experiments.