基于YOLO目标检测的烟草配送合格审查算法设计

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中图分类号：TP391.41 文献标志码：A 文章编号：1003-5168（2025）08-0042-06

DOI：10.19968/j.cnki.hnkj.1003-5168.2025.08.007

Abstract： [Purposes] Based on YOLO object detection framework，a tobacco distribution compliance review algorithm was designed to improve the compliance and accuracy of tobacco distribution process by using image recognition technology.[Methods] The algorithm uses ResNet-18 as the backbone network and combines spatial pyramid pooling （SPP） module to extract and fuse multi-scale image features. Through target detection of elements such as signers，tobacco，shelves and counters in distribution images，and setting upan audit mechanism to automaticalydetermine whether the images are compliant，automated review of distribution behavior is realized.The experimentaldata came from the standardized and non-standard distribution images screened by professionals，and the labeled data included elements such as personnel，tobacco，shelves，and counters.[Findings] The experimental results show that the A P value of the model in the category of "tobacco" is 0.893 1，and the m A P of the whole model in the test set is 0.683 9，showing good recognitionabilityand stability.[Conclusions]The research algorithmcan effctively support the inteligent distribution management of tobacco industry. In the future，the robustness and accuracy can be further improved by expanding the data set and optimizing the model to meet the needs of more complex scenarios.

Keywords： YOLO target detection; tobacco compliance detection; tobacco compliance review; image recognition

0 引言

2022年11月9日，习近平总书记在给2022年世界互联网大会乌镇峰会的贺信中指出，“当今时代，数字技术作为世界科技革命和产业变革的先导力量，日益融人经济社会发展各领域全过程，深刻改变着生产方式、生活方式和社会治理方式。（剩余6451字）