Cross refinement network with edge detection for salient object detection

IET Signal ProcessingVolume 15, Issue 7 p. 425-436 ORIGINAL RESEARCH PAPEROpen Access Cross refinement network with edge detection for salient object detection Junjiang Xiang, Junjiang Xiang School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China China Huangpu Research & Graduate School of Guangzhou University, Guangzhou, China School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorXiao Hu, Corresponding Author Xiao Hu huxiao@gzhu.edu.cn 2111807071@e.gzhu.edu.cn orcid.org/0000-0002-2252-5743 School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China China Huangpu Research & Graduate School of Guangzhou University, Guangzhou, China Correspondence Xiao Hu, School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China. Email: huxiao@gzhu.edu.cn, 2111807071@e.gzhu.edu.cnSearch for more papers by this authorJiayu Ding, Jiayu Ding China Huangpu Research & Graduate School of Guangzhou University, Guangzhou, China School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorXiangyue Tan, Xiangyue Tan China Huangpu Research & Graduate School of Guangzhou University, Guangzhou, China School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorJiaxin Yang, Jiaxin Yang China Huangpu Research & Graduate School of Guangzhou University, Guangzhou, China School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, ChinaSearch for more papers by this author Junjiang Xiang, Junjiang Xiang School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China China Huangpu Research & Graduate School of Guangzhou University, Guangzhou, China School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorXiao Hu, Corresponding Author Xiao Hu huxiao@gzhu.edu.cn 2111807071@e.gzhu.edu.cn orcid.org/0000-0002-2252-5743 School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China China Huangpu Research & Graduate School of Guangzhou University, Guangzhou, China Correspondence Xiao Hu, School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China. Email: huxiao@gzhu.edu.cn, 2111807071@e.gzhu.edu.cnSearch for more papers by this authorJiayu Ding, Jiayu Ding China Huangpu Research & Graduate School of Guangzhou University, Guangzhou, China School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorXiangyue Tan, Xiangyue Tan China Huangpu Research & Graduate School of Guangzhou University, Guangzhou, China School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorJiaxin Yang, Jiaxin Yang China Huangpu Research & Graduate School of Guangzhou University, Guangzhou, China School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, ChinaSearch for more papers by this author First published: 26 April 2021 https://doi.org/10.1049/sil2.12041AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Salient object detection aims to identify the most attractive objects from images. However, their boundaries are typically of poor quality when predicted using available methods. One or multiple objects may also be left undetected if the image contains multiple objects. To solve these problems, this article proposes the novel cross refinement network, which consists of a Res2Net-based backbone network; a fusion network equipped with four convolutional block attention modules and four edge-salient cross units; and a detection network with an edge enhancement unit and a residual refinement network (RNN). For RNN training, the rough salient maps generated using the DUTS-TR dataset are treated as a special training dataset. Compared to existing methods, the proposed network can effectively detect all objects as well as improve the boundaries of the detected objects by performing experiments on five benchmark datasets. Based on the experimental results, the proposed network outperforms existing methods both objectively and subjectively. 1 INTRODUCTION The visual attention mechanism allows human beings to screen visual information and extract regions of interest (ROIs). Salient object detection (SOD) provides a method to simulate this mechanism to extract ROIs from a specific scene [1, 2]. Currently, SOD is widely used in image understanding, facial recognition, and other fields [3]. As deep neural networks develop, significant progress has been made in SOD. Particularly, with the development of the fully convolutional network (FCN) [4], a series of FCN-based networks [5-8] have been proposed to improve the performance of SOD, which can take advantage of more high-level semantic information to widen the receptive field [5]. Thus, a salient object can be effectively highlighted by introducing more high-level semantic features in models, while the boundaries of detected objects can be refined by low-level detail features about their spatial structure. And, some certain novel methods have been proposed to optimise SOD in recent years [7, 8], which make use of the complementary information between the salient information in high-level semantic feature maps and the edge information in low-level feature maps. However, two important phenomena occur in this current network: missing objects and blurred boundaries. First, as the depth of a network increases, high-level semantic features in existing methods tend to lose significant features; thus, certain salient objects are falsely detected or missing, as shown in Figure 1d. Second, due to differences between high-level semantic features and low-level detail features, it is difficult to distinguish a boundary between objects by combining salient information and edge information. Figure 2 shows two cases of missing detection and blurred boundary in SCRN [7], AFNet [8], and BASNet [9]. To solve the problem of missed detection, researchers have developed methods that learn multi-scale features by integrating features at different levels in the network, which can effectively detect multiple objects or add attention mechanisms to focus on salient objects themselves. To resolve the problem of blurred boundary, AFNet [8] and BASNet [9] were designed with boundary enhancement loss, and SCRN [7] was designed with an edge fusion unit. To address aforementioned problems, this article builds upon existing work and proposes a novel network, which is comprised of a backbone network, four convolutional block attention modules (CBAMs) [10], four edge-salient cross units (ESCU), an edge enhancement unit (EEU), and a residual refine network (RRN). The backbone network is used to detect the salient area of an object and extract edge information preliminarily. CBAM intensifies the initially located information on the ROI, effectively dampening useless information. ESCU combines the salient information in high-level semantic features and the edge information in low-level detail features to compensate for the lost high-level semantic information and to refine edge features. EEU and RRN are then used to further refine the edges of salient objects. The contributions made by this article can be summarised as follows: (1) A novel cross refinement network (CRN) is proposed to improve SOD by decreasing the number of missing objects and improving boundaries. The proposed network includes the backbone network, the feature fusion network (CBAM and ESCU), and the detection network (EEU and RRN). (2) ESCU is proposed to aggregate high-level semantic features and low-level detail features. The proposed unit fully and effectively uses the salient information in high-level features and the edge information in low-level features, and suppresses irrelevant information. (3) A residual refine network is proposed to improve the performance of salient maps. To train RRN, the blurred binary dataset as a special training dataset is generated by GCPANet [6], which uses the DUTS-TR dataset as input. FIGURE 1Open in figure viewerPowerPoint Diagram of characteristic analysis. (b) low-level detail features show good edge information; (c) high-level semantic features show good salient information; (d) high-level features lose some salient information while adding other information. (a) Images, (b) Low-level detail feature, (c) High-level semantic feature, (d) Residual feature FIGURE 2Open in figure viewerPowerPoint Results of salient object detection. A screen (first row), 5 aeroplanes (second row), 4 cosmetic boxes (third row) and 3 flowers (fourth row) detected by SCRN [7], AFNet [8], and BASNet [9]. GT stands for ground truth. Two kinds of error results are shown in the figure: (1) missing objects. Some objects are not detected when there are several objects in an image even though these objects have a similar appearance and are on the same scale. For example, both AFNet and BASNet detect only three aeroplanes, while both SCRN and BASNet miss two flowers. (2) Blurred boundary. SCRN and AFNet fail to sketch the boundary of some objects with high-quality. Salient map supervision lacks a specific perception of boundaries, so that edge supervision is not conducted directly. (a) Images, (b) GT, (c) SCRN, (d) AFNet, (e) BASNet 2 RELATED WORKS The backbone network pre-trained by ImageNet [11] can be used to detect and classify images. However, to make the backbone network more suitable for SOD, certain modules must be added. To mitigate missing objects and blurred boundaries in salient detection, existing SOD methods can be classified into three categories: feature aggregation-based methods, edge-aware-based methods, and attention-based methods. 2.1 Feature aggregation-based methods After an image has been input to a backbone network, several layers of feature maps are typically output. Feature maps from different layers include different information. For example, low-level feature maps include more details, while high-level feature maps consider global information. Importantly, feature aggregation can improve salient object detection. 2.1.1 One-way aggregation Zhang et al. [12] took a standard convolution module (convolution + BN + activation function) as a backbone network. Gradually transitioning from low-level to high-level features, salient objects tend to become more predominant. Researchers detect salient objects from the highest-level feature maps, while the one-way aggregation strategy tends to lose details. 2.1.2 Side aggregation Luo et al. [13] and Hou et al. [14] converged multi-scale features into a feature map and then detected objects from the fused feature map. Side aggregation can supervise the features of different layers but ignores the difference between the high-level semantic information and the low-level detail information. Li et al. [15] designed a V-shaped encoder module, where detailed features are gradually integrated with the semantic features in each decoder layer. Liu et al. [16] proposed a new fusion strategy, which multi-level contextual information can be learnt to generate accurate properties of the salient object. 2.1.3 Bidirectional aggregation Song et al. [17] proposed to learn backward information on top of forward information. Feng et al. [8] used an encoder-decoder architecture to aggregate features from different layers. Salient features are bidirectionally aggregated between encoders and decoders. Although these algorithms can achieve better performance, their structures are complex, and they require more computation time. Additionally, common feature aggregation methods integrate depth information into the network [18]. A novel descriptor, called depth shape prior, is used in the addition scheme to introduce depth information to enhance identification of co-salient objects [19]. The effect of the depth feature map on salient detection and how to combine depth features effectively is discussed by Cong et al. [20] and Chen et al. [21]. 2.2 Edge-aware-based methods Wu et al. [7] designed SCRN to refine salient and edge features in multi-layer features simultaneously. Information is subject to bidirectional transmission between two features. Liu et al. [22] added three edge detection branches based on a residue block and a convolution module. Each branch outputs 16 feature maps, making 48 feature maps in total, from which edge information is captured. Qin et al. [9] used the residual encoder-decoder architecture to develop a RRM to refine the edges of rough salient maps; a hybrid loss function for the RRM was also proposed to detect pixel-level edges. To resolve a narrow background boundary between foreground regions, Feng et al. [8] designed a boundary enhancement loss. Existing algorithms based on edge guidance typically optimise the edge of salient prediction maps by designing an edge loss function or designing edge detection module. However, these methods ignore the influence of edge features carried by feature maps in different layers on the prediction maps of salient maps. Therefore, we proposed an ESCU module to consider the influence of edge features of different layers on the prediction network. 2.3 Attention-based methods The attention mechanism can enhance salient object information and suppress useless information [23]. Zhao et al. [24] proposed a pyramid feature attention network to focus on multi-scale features from different levels. Chen et al. [25] devised a top-down reverse attention module to guide the features of a multi-scale context module to output. However, this strategy does not effectively use other information from the prediction map. More recently, Li et al. [26] introduced a cyclic reverse attention module into the SOD network. The cyclic reverse attention module focuses on salient areas and residual areas outside the salient areas. Additionally, this module can guide the network to learn salient parts and missing details. In recent years, depth maps with RGB images have been captured to improve the SOD performance. Li et al. [27] used the attention-guided mechanism to fuse RGB images and depth maps. The global context-aware attention mechanism was designed by Zhang et al. [28] to learn more discriminative features. 3 PROPOSED CRN Clear boundaries and accurate targets are favourable for salient object detection in many fields, such as image classification and segmentation. However, the phenomena of missed detection and blurred boundary introduce noise into the algorithm. Thus, this article proposes a novel Cross Refinement Network. The novel CRN consists of a backbone network based on Res2Net [29], which is a feature fusion network, and an object detection network (ODN). The CRN's structure is shown in Figure 3. First, the backbone network uses four layers to form four feature maps. Then, the four feature maps are fused by the fusion network. Finally, salient objects are detected and improved by the detection network. The fusion network holds four CBAM and four ESCU. The detection network includes the EEU and RRN. These components will be described in the following sections. FIGURE 3Open in figure viewerPowerPoint Network structure of the proposed CRN. The feature fusion component consists of CBAM and ESCU, and the object detection component consists of EEU and RRN. S0 and E0 represent the rough salient features and edge features, respectively. S and E indicate the salient features and edge features refined by ESCU, respectively. L E denotes the loss between the estimated edge information and edge ground truth (GT), L S refers to the loss between the estimated salient information and salient GT, and L S E stands for the loss between edge GT and the edge information extracted from salient objects. CBAM, convolutional block attention module; CRN, cross refinement network; EEU, edge enhancement unit; ESCU, edge-salient cross unit; RRN, residual refinement network 3.1 Convolutional block attention module In 2018, Woo et al. [10] developed a CBAM for image classification and object detection applications, and achieved good performance. It is worth noting that we use the CBAM to improve latent object detection. As showed in Figure 4, CBAM is comprised of a channel attention (CA) block and a spatial attention (SA) block. Input features pass through the 1D CA block and 2D SA block in succession. After the input feature maps, Fin, are input to CBAM, the CA block attempts to identify more useful channels and then creates a weight vector, M c ( F i n ) , which sets the most useful channel to the highest value and the most useless channel with the lowest value. Then, the weights in the vector are multiplied by its corresponding channel of the input feature map. Then, the weighted feature maps are transformed into SA maps using MaxPool, AvgPool, and convolution in the SA block. The transformation relationship can be described as: F o u t = M S ( M c ( F i n ) ⊗ F i n ) ⊗ M c ( F i n ) ⊗ F i n (1)where M c ( ⋅ ) and M s ( ⋅ ) represent the project function of the CA block and the SA block respectively, each one can be shown by Equations (2) and (3); ⊗ denotes dot product; and F o u t is the output feature map: M c ( x ) = σ ( M L P ( M a x P o o l ( x ) ) + M L P ( A v g P o o l ( x ) ) ) (2) M s ( x ) = σ ( f 7 × 7 ( C a t ( A v g P o o l ( x ) , M a x P o o l ( x ) ) ) ) (3)where x represents the input map; M L P ( ⋅ ) indicates a multi-layer perceptron with weight sharing; A v g P o o l ( ⋅ ) denotes the average pooling; M a x P o o l ( ⋅ ) refers to the maximum pooling; σ stands for nonlinear activation function; and f (7 × 7) represents a convolution operation and the size of the convolution kernel is (7, 7). Features from the backbone network are inputted into CBAM, producing the enhanced feature which is then divided into an edge feature E0 and a salient feature S0. FIGURE 4Open in figure viewerPowerPoint Convolutional block attention module 3.2 Edge-salient cross unit Edge information and salient information are fused by CRU in SCRN. However, this strategy simply achieves cross fusion; thus, edge information is incomplete in the high-level edge information map. If this map were fused with the low-level salient information, the boundary of the salient prediction maps would blur. Therefore, this article proposes the following improved cross fusion strategy: only the edge features of the first–third layers are fused with the salient features, and only the salient features of the second–fourth layers are fused with the edge features. Figure 5 shows the improved unit. The salient information of the high-level feature ( H S n j ) and the edge information of low-level feature ( L E n i ) of the nth ESCU and the ith or jth layer are calculated by aggregating features: H S n j = H S n − 1 j + S ( H S n − 1 j , L E n − 1 ) (4) L E n i = L E n − 1 i + E ( L E n − 1 i , H S n − 1 ) (5)where S ( ⋅ ) represents the salient aggregating function that fuses low-level edge information into salient information, and E ( ⋅ ) denotes the edge aggregating function that fuses high-level salient information into edge information. The specific formulae are shown in Equations (6) and (7): S ( ⋅ ) = f ( H S n − 1 j ⊗ ∏ k = 1 3 f a d ( L E n − 1 k ) ) (6) E ( ⋅ ) = f ( C a t ( L E n − 1 i , C a t k = 2 4 [ f a d ( H S n − 1 k ) ] ) ) (7)where f a d ( ⋅ ) is a convolution operation that adjusts the channel and feature size to unify the spatial size of salient features and edge features, and f ( ⋅ ) stands for convolution operation. Equation (6) and Figure 5b present the M u l operation, and Equation (7) and Figure 5a present C a t operation. To detect salient objects and refine edges, salient features are delivered to the detection network. FIGURE 5Open in figure viewerPowerPoint Edge-Salient Cross Units. ⊕ indicates add operation. ⊗ denotes dot product. © refers to concatenation. n ∈ (1, 2, 3, 4) means the number of stacked ESCU. i and j represent the layers in backbone network after CBAM. (a) Cat, (b) Mul. CBAM, convolutional block attention module; ESCU, edge-salient cross unit 3.3 Edge enhancement unit To compensate for the lost edge information for the salient object, Wu et al. [7] proposed cross-fusion to integrate salient features with edge features. However, a simple combination does not effectively guide edge detection from the predicted salient region. After ESCU produces the salient region S and its edge E, enhancement edge e is detected from the generated saliency region S by the Sobel operator, which is referred to as EEU in this article. Then, binary cross-entropy loss is used in this article, as shown in Equation (8): L = − ∑ i = 1 n p ( x i ) log ( q ( x i ) ) + ( 1 − p ( x i ) ) log ( 1 − q ( x i ) ) (8)where p ( x i ) and q ( x i ) represent the GT and estimated value at pixel i, respectively. To train the proposed network, the structure of loss function in this article is designed as follows: L = L e + L s + L s e (9)where L e represents the loss between the estimated edge information and the edge GT; L s indicates the loss between the estimated salient information and the salient GT; and L s e denotes the loss between the edge GT and the edge information extracted from salient objects. 3.4 Residual refine network To optimise the blurred binary images, RRM in BASNet [9] is used to construct an edge optimization module, in which feature maps with 64 input channels are synthesised into a predicted salient map with excellent boundaries. RRM is used to design a new network known as RRN, in which the input and output channels are both set to 1, and the network structure consists of four encoding-decoding structures. The DUTS-TR dataset is used to generate a set of salient feature maps with blurred edges for training RRN. Figure 6 shows three salient feature maps with blurred edges. RRN is independently trained with a fine-tuning training method, and then the trained RRN is equipped with the proposed network. The two-stage training strategy reduces the difficulty in training the end-to-end network. FIGURE 6Open in figure viewerPowerPoint Blurred dataset, a set of salient feature maps with blurred edges. (a) Images, (b) Blurred data, (c) GT. GT, ground truth 4 EXPERIMENTS All algorithms are operated on Ubuntu 14.04.3 LTS, Python 3.6, and PyTorch (0.4.0), and a 1080Ti GPU is used for acceleration. Other settings include the SGD algorithm, 30 iterations, and 0.002 learning rate. However, the learning rate is multiplied by 0.1 after 20 iterations to achieve network convergence. 4.1 Datasets and evaluation metrics Five common evaluation metrics are used in these experiments: mean absolute error (MAE), F-measure (Fmax), enhanced-alignment measure (Em) [30], weighted F β measure (Wfm) [31] and precision-recall (PR). Five common datasets, including ECSSD [32], DUT-OMRON [33], PASCAL-S [34], HKU-IS [35], and DUTS [36], are chosen to evaluate the proposed network. ECSSD contains 1000 images, DUT-OMRON contains 5168 images, PASCAL-S contains 850 images and HKU-IS contains 4447 images. With the most images, DUTS is classed into DUTS-TR (10,553 images) as a training dataset and DUTS-TE (5019 images) as a test dataset. In SOD, only DUTS-TR is used to train the network, and other images are used to verify the network. 4.2 Ablation study 4.2.1 Aggregation methods Based on different combinations of M u l and C a t , four operations are performed. Table 1 shows the aggregation relationship between edge features of the first and second layers, and the salient features of the third and fourth layers. For example, C a t − M u l indicates that each ESCU produces edge features based on Equation (6) and salient features based on Equation (7). From Table 1, we know that achieves the best performance among the four combinations, which is superior to the baseline (SCRN). In the proposed network, M u l − C a t is used in M u l − C a t each ESCU. For a fair comparison with CRU, we used ResNet as the backbone first. TABLE 1. Aggregation methods. Mul and Cat represents different aggregate in Equation (6) and (7) S ( ⋅ ) − E ( ⋅ ) PASCAL-S DUTS-TE Fmax↑ MAE↓ Wfm↑ Em↑ Fmax↑ MAE↓ Wfm↑ Em↑ Cat–Mul 0.865 0.069 0.790 0.842 0.884 0.043 0.789 0.875 Cat–Cat 0.862 0.070 0.784 0.841 0.863 0.046 0.767 0.870 Mul–Mul 0.866 0.069 0.789 0.849 0.881 0.042 0.789 0.880 Baseline (SCRN) 0.867 0.068 0.793 0.842 0.878 0.044 0.791 0.879 Mul–Cat 0.869 0.065 0.795 0.848 0.885 0.043 0.795 0.878 Abbreviation: MAE, mean absolute error. 4.2.2 Fusion of high- and low-level information The three levels of the fusion strategy are shown in Table 2. (1) (1,2–3,4) Edge information obtained from the first and second layers are combined with salient information. Salient information from the third and fourth layers is combined with edge information; (2) (1,2,3–2,3,4) Edge information obtained from the first-third layers is combined with salient information. Salient information from the second-fourth layers is combined with edge information; (3) (1,2,3,4–1,2,3,4) Edge information obtained from the first-fourth layers is combined with salient information. Salient information from the first-fourth layers is combined with edge information; Table 2 shows that the secondary fusion strategy (1,2,3–2,3,4) performs best. To compare and test the performance of the proposed ESCU module, this article combined ESCU with different backbone networks, and the detection effect is shown in the Figure 7. The structure of Res2Net + ESCU used in this article achieves better performance. However, as shown in Figure 7d, missed detections remain in the detection images. Therefore, the attention mechanism is introduced to allow the algorithm to consider salient detection. TABLE 2. Fusion of high-level and low-level information. We use different layers of information to aggregate, and i and j represent the layers in the backbone network after CBAM at the end of the experiment L S i – H S j PASCAL-S DUTS-TE Fmax↑ MAE↓ Wfm↑ Em↑ Fmax↑ MAE↓ Wfm↑ Em↑ (1,2–3,4) 0.869 0.065 0.795 0.848 0.885 0.043 0.795 0.878 (1,2,3–2,3,4) 0.874 0.065 0.802 0.855 0.887 0.039 0.804 0.892 (1,2,3,4–1,2,3,4) 0.870 0.068 0.796 0.855 0.884 0.040 0.798 0.889 Abbreviations: CBAM, convolutional block attention module; MAE, mean absolute error. FIGURE 7Open in figure viewerPowerPoint ESCU with different backbone. (a) Images, (b) GT, (c) Res2Net, (d) ResNet, (e) seResNet. ESCU, edge-salient cross unit; GT, ground truth 4.2.3 Attention mechanism Tables 1 and 2 verify the performance of ESCU. To optimise the network structure, we use Res2Net and the attention mechanism. The attention mechanism is divided into CA and SA. The former assigns different weights to different channels, and the later distributes weights onto the different positions of each feature map. In DANet [37], the attention mechanism with channel attention and position attention is applied to the feature map in the last layer. Because we may apply the attention mechanism to the features from other layers, we perform several ablation experiments. Table 3 shows different attention mechanisms, where CBAM with both CA and SA achieve better performance if the attention mechanism is applied to all features from all layers. TABLE 3. Attention mechanism. We explore different mechanisms of attention and the strategy of feature aggregation PASCAL-S DUTS-TE Methods Fmax↑ MAE↓ Wfm↑ Em↑ Fmax↑ MAE↓ Wfm↑ Em↑ 3rd + 4th layers DANet 0.868 0.072 0.787 0.829 0.876 0.048 0.779 0.865 DANet(Channel) 0.872 0.067 0.798 0.842 0.888 0.043 0.798 0.880 DANet(Position) 0.874 0.069 0.789 0.842 0.880 0.047 0.776 0.869 CBAM(CA + SA) 0.872 0.065 0.801 0.851 0.893 0.041 0.806 0.883 CBAM(CA) 0.880 0.066 0.805 0.847 0.888 0.043 0.802 0.878 CBAM(SA) 0.875 0.066 0.799 0.858 0.892 0.040 0.805 0.891 Alright layers CBAM(CA + SA) 0.875 0.064 0.807 0.853 0.894 0.040 0.812 0.888 Abbreviations: CA, channel attention; CBAM, convolutional block attention module; MAE, mean absolute error; SA, spatial attention. 4.2.4 Optimization strategy In addition to fine-tuning the parameters of the network structure, this article takes advantage of EEU and RRN to optimise the prediction maps. EEU is used to extract edge information directly from the predicted salient maps. However, to build an effective EEU, we apply a