Saliency-based YOLO for single target detection

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 x 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.

Automated summary

The YOLO3-SM method improves object detection performance by introducing a saliency map, achieving higher IOU and mAP values compared to YOLO3.