Salient object detection (SOD) has made great progress, but most of existing SOD methods focus more on performance than efficiency. Besides, the U-shape structure exists some drawbacks and there is still a lot of room for improvement. Therefore, we propose a novel framework to treat semantic context, spatial detail and boundary information separately in the decoder part. Specifically, we propose an efficient and effective Complementary Trilateral Decoder (CTD) for saliency detection with three branches: Semantic Path, Spatial Path and Boundary Path. These three branches are designed to solve the dilution of semantic information, loss of spatial information and absence of boundary information, respectively. These three branches are complementary to each other and we design three distinctive fusion modules to gradually merge them according to "coarse-fine-finer" strategy, which significantly improves the region accuracy and boundary quality. To facilitate the practical application in different environments, we provide two versions: CTDNet-18 (11.82M, 180FPS) and 110FPS). Experiments show that our model performs better than state-ofthe-art approaches on five benchmarks, which achieves a favorable balance between speed and accuracy. CCS CONCEPTS• Computing methodologies → Interest point and salient region detections.

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