基于深度学习的物流跟踪管理

Logistics Tracking Management Based on Deep Learning

利用先进的人工智能和计算机视觉技术,物流管理取得了重大进展。如何建立一套能够有效解决物体遮挡、运动模糊、目标相似等实际问题的检测技术,是一个重要的挑战。文章提出了一种基于YOLOv8和Deep-SORT的方法来跟踪货物位置。该系统可以有效地识别、定位、跟踪和计数镜头前的货物。称之为“warehouse management”,该算法基于示例跟踪范式,并将跟踪应用于检测对象的边界框。在此基础上,自动识别感兴趣区域(ROI),有效消除不需要物体。我们的F1的分数是0.816 7。

Utilizing advanced artificial intelligence and computer vision technology,logistics management has made significant progress.How to establish a set of detection technology that can effectively solve the practical problems such as object occlusion,motion ambiguity and target similarity is an important challenge.This paper presents a method based on YOLOv8 and Deep-SORT to track cargo location.The system can effectively identify,locate,track and count goods in front of the lens.It is called“warehouse management” and the algorithm is based on the example trace paradigm and applies the trace to the bounding box of the detected object.Based on this,the Region of Interest(ROI) is automatically identified,effectively eliminating unwanted objects.Our F1 score is 0.816 7.