# Application of Split Coordinate Channel Attention Embedding U2 Net in Salient Object Detection
## 1 Introduction  
In an era of rapidly increasing data volume and diverse application scenarios, traditional machine learning methods have exhibited significant limitations in processing image data. As a result, deep learning, which relies on massive data, has gradually become the mainstream approach. Over the past few years, deep learning methods have consistently outperformed previous state-of-the-art machine learning techniques in various domains, with computer vision being one of the most prominent examples [1, 2, 3]. Computer vision is a crucial field within artificial intelligence which studies how computers acquire, process, analyze, and understand digital images [4, 5]. The primary research areas in computer vision include image classification, object detection, image segmentation, object tracking, scene understanding, image retrieval, image matching, and image registration. Inspired by the human biological system, an attention mechanism tends to focus on individual parts when processing large amounts of information. In cases of limited computing power, an attention mechanism can be used as a resource allocation scheme to prioritize the processing of more important information [6, 7]. Furthermore, attention introduces a form of explanation to neural network models, which are often considered highly complex [8]. Attention mechanisms in neural networks have been utilized in a wide range of tasks, such as image caption generation, text classification, machine translation, action recognition, and speech recognition. The channel attention mechanism learns theweight relationship of each pixel within a single channel dimension and applies it to all pixel regions [9]. Attention mechanisms can adaptively focus on important regions by weighting image features at different locations and channels. However, typically only a single attention mechanism is utilized. Salient object detection simulates the visual attention and aims to extract the most informative objects and regions in the scene, and then combines this local information to effectively understand the whole scene. Normally, the main approaches can be summarized as locating salient objects and drawing the outline. Salient object detection has been widely used in various computer vision tasks, such as stereo matching, image understanding, co-saliency detection, action recognition, video detection and segmentation, etc. In recent years, FCN-based encoder-decoder models have achieved success in pixel-level dense prediction tasks, but many challenges still remain. (1) The contribution of different features to salient objects is not considered, and redundant information such as noise from low layers and fuzzy boundaries from high layers will lead to accuracy degradation. (2) In the saliency detection task, the binary cross-entropy (BCE) loss is usually used as the loss function to train the relationship between the supervised saliency map and the ground truth. However, the BCE loss treats each pixel equally and often has low confidence. (3) The importance of context information for extracting salient regions is ignored, resulting in the omission of part of the object. The current strategies for improving salient object detection algorithms include fusion models, single-stream/multi-stream models, attention modules, and more. In order to effectively learn the features of RGB images and depth images simultaneously and explore the correlation between RGB images and depth maps, various models fuse RGB images and depth maps using different strategies [10; 11; 12]. This paper proposes a split coordinate channel attention (SCCA) that divides the input feature map channels in half. SCCA performs convolution operation and width and height pooling aggregation, effectively extracting feature information from the image and determining the position information of the target. This paper introduces the Canny edge detection loss [13] to redefine the loss function in salient object detection.