SN-YOLO: Improved YOLOv5 with Softer-NMS and SIOU for Object Detection

Abstract

As a lightweight target detection network, YOLOv5 is popular in the industry for its advantages of fast speed and small model, but the detection accuracy is not very high. In response to this problem, we propose an improved model SN-YOLO based on YOLOv5. First, we introduce Softer-NMS as the post-processing method of the model, which will make the prediction box more accurate. Secondly, we improved the loss function of the original algorithm and introduced the SIOU loss function to optimize the model and improve the accuracy of the algorithm. Finally, in order to improve the feature extraction ability of the backbone, we implanted the CBAM (Convolutional block attention module) module into the algorithm. We validate the model using the 2007 and 2012 datasets of PASCAL VOC. The experimental results show that SN-YOLO has a great improvement over the original model in all aspects. The effectiveness of the algorithm is verified.