Improve Multi-Object Tracking Using YOLOv5 and Deep-SORT for Chinese Yam Counting Model

Abstract

Accurate counting information is important for crop yield and quality. Obtaining yield estimation of Chinese Yam is critical to improving productivity. Referring to the method of counting pedestrian flow in surveillance video and its accuracy, a method for counting the number of Chinese Yams harvested in the field is proposed by improving YOLOv5s detection combined with Deep-SORT tracking. In order to improve the recognition effect of the detector, the attention module CBAM is fused with the Neck part of the YOLOv5s network to improve the feature extraction ability of the network; CIoU Loss is used instead of GIoU Loss as the target bounding box regression loss function to speed up the bounding box regression rate while improving positioning Accuracy; use DIoU-NMS to replace NMS to improve the missed detection problem when the target is crowded.Adjust the structure of the Deep SORT appearance feature extraction network and retrain on the yam re-identification dataset to reduce the identity switching caused by target occlusion. Connect the improved YOLOv5s detector and Deep SORT, and set a virtual detection line in the video to count the number of yams.The experimental results show that the number of yams can be counted more accurately. Compared with the original algorithm, the improved YOLOv5s has an average accuracy rate of 2.1 percentage points. Combined with Deep-SORT tracking, the counting statistics accuracy rate reaches 92.7%.