The Performance Analysis of Lightweight Neural Networks for Salient Object Detection

Nowadays, salient object detection (SOD) has become a prominent research area in computer vision, which has various applications in real life. With the advancement of convolutional neural networks (CNN), numerous SOD methods have been proposed to imitate the human visual system to identify salient objects in images. Their marvelous performance comes with large models, high computational complexity, and memory consumption, raising many challenges to deploy these models on practical applications and low-resource devices. Under the high demand for developing efficient models for SOD tasks, in this paper we provide a comprehensive performance analysis focusing on existing CNN-based lightweight SOD models in terms of accuracy and efficiency. By discussing recent methods regarding the proposed ideas and main techniques, we analyze the current issues of lightweight SOD models in detail. Various metrics are considered for performance assessment followed by future research directions and conclusion.