The model pruning is one of the predominant model compression tasks to decrease the demands in computing power and memory footprint. However, most existing pruning methods have overly broad application areas, which defects in a sub-optimal solution specifically to solve certain specified difficult problems in the tasks of salient object detection. In this paper, we propose a novel solution, dubbed as WRGPruner, based on the concept of salient energy level (SEL) for tiny salient object detection. The concept of SEL defines the level of assessing the distinguishing ability of parameters in the trained model between background and salient objects. To exploit the SEL, the WRGPruner is proposed, which considers three factors for model compression including the weight in the filter, the mathematical rank of the feature map matrix, and the gradient in the backward propagation. We mathematically prove the effectiveness of the WRGPruner for tiny salient objects. Besides, a tiny salient object dataset (TSOD) is constructed for evaluation. Extensive experiments show that WRGPruner reduces 60% of parameters with slight enhancement in terms of six accuracy metrics for VGG16 on TSOD. This demonstrates that the SEL is suitable for measure parameters and the effectiveness of WRGPruner.

模型修剪是减少模型对计算能力和内存占用的要求的主要模型压缩任务之一。然而，大多数现有的修剪方法具有过大的应用领域，这在次优解决方案中存在缺陷，该解决方案专门用于解决显着物体检测任务中某些特定的难题。在本文中，我们基于显着能级检测的显着能级（SEL）概念，提出了一种称为WRGPruner的新颖解决方案。SEL的概念定义了评估受训练模型中背景和显着对象之间的参数区分能力的级别。为了开发SEL，提出了WRGPruner，它考虑了模型压缩的三个因素，包括权重在滤波器中，特征图矩阵的数学等级以及向后传播中的梯度。我们从数学上证明了WRGPruner对于微小凸出物体的有效性。此外，还构建了一个微小的显着目标数据集（TSOD）进行评估。大量实验表明，WRGPruner减少了60％的参数，并在TSOD上的VGG16的六个精度指标方面有所提高。这表明SEL适用于测量参数和WRGPruner的有效性。