# Multiplexing Multi-Scale Features Network for Salient Target Detection
## 1 Introduction  
Salient object detection (SOD) refers to the detection and extraction of significant objects with the most focused human visual attention from complex scenes [1]. With the advancement of science and technology, salient object detection has become a prevalent method of image pre-processing in a variety of computer vision tasks, such as image classification [2], semantic segmentation [3], traffic control [4], object detection [5, 6], pedestrian detection [7], etc., which helps to find effective objects or regions in a scene. Significance detection methods can be classified into traditional methods and deep learning-based methods. Traditional methods define significance detection as a binary segmentation problem, as shown in Figure 1.  
They utilize position, orientation, spatial, and depth priors to construct an initial computational framework model, which is then used to complete the task of salient target detection. These methods often require manual labeling as a means of guidance, and their detection results are often inaccurate [9, 10]. To address this limitation, the researchers have introduced neural networks into salient target detection algorithms and integrated multi-scale features into the network to enhance the capture of salient information. [PERSON] et al. [11] proposed a multi-scale feature pyramid network that controlled the sampling step size of a basic network to enhance the fusion strength of multi-scale features. [PERSON] et al. [12] introduced dual feature aggregation modules in their multi-scale-feature, deep reuse, significance target detection network to reuse CNN features through two pathways and perform feature enhancement using residual connections. [PERSON] et al. [13] introduced a newfusion of multi-scale semantic information to enhance the expression of shallow features and to strengthen the shallow features of targeted areas through an attention mechanism, so as to obtain better detection results. [PERSON] et al. [14] proposed a method for processing input images. This involves subjecting the images to superpixel segmentation, extracting and optimizing pre-significant regions using data processing, and then performing a principal component analysis on high-dimensional depth features using feature extraction. Finally, the significance value is calculated. However, most of these methods merely involve the simple reuse of multi-scale features, representing only a preliminary fusion of different features. They do not deeply explore the intricate relationships between multi-scale features. Establishing a new network structure that further investigates the deeper connections between multi-scale features, building on this initial feature fusion, to enhance the performance of salient object detection is a topic worthy of thorough research. This forms the first motivation of this study. The multiplexing of multi-scale features can prompt networks to generate more accurate saliency maps [15]. However, the excessive integration of feature maps with different resolutions not only increases computational burden, but also leads to the dilution of useful features, which can ultimately compromise the algorithm's performance [16]. In order to address this issue, numerous alternative approaches have been put forth by researchers. [PERSON] et al. [17] proposed a two-way multi-scale pooling target detection network to extract the semantic information of their data and realize feature mapping, so as to enhance the feature representation ability of weak targets and avoid performance loss. [PERSON] et al. [18] proposed an infrared image weak-target detection algorithm with high computing efficiency. This algorithm integrates a mathematical formula into its process, preprocesses the data, and employs a two-way multi-scale pooling target detection network to enhance the feature representation ability of weak targets. [PERSON] et al.