At present, You only look once (YOLO) is the fastest real-time object detection system based on a unified deep neural network. During training, YOLO divides the input image to \(S \times S \) gird cells and the only one grid cell that contains the center of an object, takes charge of detecting that object. It is not sure that the cell corresponding to the center of the object is the best choice to detect the object. In this paper, inspired by the visual saliency mechanism we introduce the saliency map to YOLO to develop YOLO3-SM method, where saliency map selects the grid cell containing the most salient part of the object to detect the object. The experimental results on two data sets show that the prediction box of YOLO3-SM obtains the lager IOU value, which demonstrates that compared with YOLO3 , the YOLO3-SM selects the cell that is more suitable to detect the object . In addition, YOLO3-SM gets the highest mAP that the other three state-of-the-art object detection methods on the two data sets, which shows that introducing the saliency map to YOLO can improve the detection performance.

目前，“您只看一次”（YOLO）是基于统一的深度神经网络的最快的实时对象检测系统。在训练过程中，YOLO将输入图像划分为\（S \ times S \）网格单元和唯一一个包含对象中心的网格单元负责检测该对象。不确定与对象中心相对应的单元格是检测对象的最佳选择。在本文中，受视觉显着性机制的启发，我们将显着性图引入YOLO以开发YOLO3-SM方法，其中显着性图选择包含对象最显着部分的网格单元以检测对象。在两个数据集上的实验结果表明，YOLO3-SM的预测框获得了更大的IOU值，这表明与YOLO3相比，YOLO3-SM选择了更适合检测对象的单元。此外，在两个数据集上，YOLO3-SM获得的mAP最高，是其他三种最新的对象检测方法所能达到的，