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Salient Object Detection: An Accurate and Efficient Method for Complex Shape Objects
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DOI：10.1109/ACCESS.2021.3138782.png)
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Abstract
En 中文
Deep learning-based salient object detection (SOD) methods have made great progress in recent years. However, most deep learning-based methods suffer from coarse object boundaries and expensive computations, especially in detecting objects with complex shapes. This paper presents an accurate and efficient SOD method that is based on a novel double-branch network that includes a body branch and an edge branch. To obtain an accurate edge, an edge profile enhancement module (EPEM) is embedded in the edge branch. In addition, a fusion feedback module (FFM) is embedded to integrate features from the two branches. To address the problem of expensive computations, channel attention module (CAM) is included to restrain redundant feature channels. Thus, the speed of the inference step can be improved with little reduction in the boundary accuracy. Experimental results on 9 datasets demonstrate that the proposed method performs favorably against 8 state-of-the-art methods in terms of both accuracy and efficiency. Additionally, our method achieves excellent detection results for objects with complex shapes.
Keywords:
Image edge detection
Feature extraction
Shape
Object detection
Convolutional neural networks
Semantics
Saliency detection
Salient object detection
complex shape object
edge profile enhancement module
channel attention module
fusion feedback module
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
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