Abstract: Small object detection has always been a difficult point in the field of object detection.In response to the high installation of cameras in cigarette factory packaging rooms,low accuracy of small object detection,and overall low detection accuracy,an improved YOLOv8n object detection algorithm YOLOv8n FIAL has been proposed.Firstly,the C2fg module with added channel rearrangement mechanism is used to replace the original C2f module to improve feature learning ability.The adaptive channel feature fusion module is used to replace the Concate operation in the Neck section of the YOLOv8n algorithm,making feature fusion more comprehensive;then,add a small target detection layer to improve the accuracy of small target detection and reduce the missed detection rate;finally,the Focal EIOU loss function is used to replace the original CIOU loss function.The number of high-quality anchor boxes with a large overlap between the balanced anchor box and the real box is much less than the problem of imbalanced training instances of low-quality anchor boxes.The experimental results show that on the self-made cigarette factory worker violation operation dataset,the YOLOv8n FIAL detection method proposed in this article has an overall average accuracy improvement of 7.6% compared to the original YOLOv8n method.The average accuracy improvement for the three types of small targets,namely mouth,nose,handheld phone,and clothing collar,is the largest,with increases of 8.3%,8%,and 9.6%,respectively;On the public dataset VOC2007,the YOLOv8n FIAL algorithm has an overall average accuracy improvement of 1.6% compared to the YOLOv8n algorithm.

Key words: Rolling car room, Small target detection, YOLOv8n, YOLOv8n FIAL, Adaptive channel feature fusion module