Studies Advanced in Salient Object Detection based on Deep Learning
DOI:
https://doi.org/10.54097/hset.v39i.6761Keywords:
Salient Object Detection; Generating Adversarial Networks; Convolutional Neural Network; Recurrent Neural Network; Computer Vision.Abstract
A very important job in the area of computer vision is salient object detection, its goal is to check salient or obvious objects in images or videos. Thanks to rapidly developing networks of convolutive neurons, especially generative adversarial networks, CGAN has become a popular framework for salient object detection, which has promoted breakthroughs in detection precision and velocity of protruding objects. Focusing on the topic of salient object detection, in this paper, we systematically introduce the research progress of salient object detection. Specifically, we first introduce methods for detecting protracted objects based on general object detection algorithms (such as Fast R-CNN, YOLO, and SSD), including their basic ideas, key steps, advantages and disadvantages, etc. We then focus on CGAN-based salient object detection methods, including method classification, typical method introduction, and application examples. Finally, we quantitatively compare the performance of different saliency detection algorithms on common datasets, and discuss the existing research problems and feasible solutions of the topic.
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