An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy

Abstract

For deep learning-based object detection, we present a superior network named MSS-YOLOv5, which not only considers the reliability in complex scenes but also promotes its timeliness to better adapt to practical scenarios. First of all, multi-scale information is integrated into different feature dimensions to improve the distinction and robustness of features. The design of the detectors increases the variety of detection boxes to accommodate a wider range of detected objects. Secondly, the pooling method is upgraded to obtain more detailed information. At last, we add the Angle cost and assign new weights to different loss functions to accelerate the convergence and improve the accuracy of network detection. In our network, we explore four variants MSS-YOLOv5s, MSS-YOLOv5m, MSS-YOLOv5x, and MSS-YOLOv5l. Experimental results of MSS-Yolov5s show that our technique improves mAP on the PASCAL VOC2007 and PASCAL 2012 datasets by 2.4% and 2.9%, respectively. Meanwhile, it maintains a fast inference speed. At the same time, the other three models have different degrees of performance improvement in terms of balancing speed and precision in challenging detection regions.