Salient object detection based on adaptive recalibration technique through deep network

Object detection is most required task in computer vision to meet the requirement of autonomous processes. The key challenge in object detection is to separate the foreground known as dominant information of the image from background. In this paper, a new model Squeeze and Excitation Region-Convolutional Neural Network (SER-CNN) is proposed which combines a Squeeze and Excitation (SE) embedded CNN with a Region proposal CNN (R-CNN) to detect salient objects. In SER-CNN, SE blocks are embedded in CNN and this extracts features made up of grand pixels which are accompanied with channel information. R-CNN generates salient map by fusing grand pixels with bounding box techniques to detect objects which is similar to the ground truth. DUT-OMRON, Pascal VOC and SOD dataset applied to find the performance of the SER-CNN. MAE and maxF performance evaluation measures are used for comparing the proposed work with the existing ones.