用于校园垃圾检测的合成与派生训练图像：基于YOLOv8n的多种子评估

中文摘要 AI 辅助

不正确的垃圾处理会污染校园回收流程，安装在垃圾桶上的摄像头可在物品丢弃时提供反馈。我们评估了合成图像和派生图像是否能改进用于此场景的YOLOv8n检测器。真实数据集包含148张校园照片，12种联合训练配置改变了添加图像的数量和来源。通过四个匹配种子重复七种主要设置并计算自展百分位数区间。仅使用真实图像的模型平均mAP@0.5为0.691 [0.665, 0.722]，背景替换使其降至0.560 [0.499, 0.619]，孤立物体图像为0.680 [0.644, 0.724]，完整增强池为0.487 [0.438, 0.537]。还测试了手和前臂合成图像，校正后的配对差异为+0.034 [-0.063, 0.199]，不支持可靠的手合成效果。单种子转移实验产生了与源相关的联合混合和顺序预训练排名。没有评估配置超过仅使用真实图像的基线。报告的区间量化了种子变化；31张照片的测试集对于得出特定类别的有力结论来说太小。

英文摘要

Incorrect disposal can contaminate campus recycling streams, and a bin-mounted camera could provide feedback as an item is discarded. We evaluated whether synthetic and derived images improve a YOLOv8n detector for this view. The real dataset contained 148 campus photographs: 86 for training, 31 for validation, and 31 for testing. Twelve joint-training configurations varied the amount and source of added images. We repeated seven principal settings with four matched seeds and computed bootstrap percentile intervals over those seeds. The real-only model reached a mean mAP@0.5 of 0.691 [0.665, 0.722]. Background replacement reduced the mean to 0.560 [0.499, 0.619], isolated-object images gave 0.680 [0.644, 0.724], and the full augmentation pool gave 0.487 [0.438, 0.537]. We also tested hand-and-forearm composites because every real photo showed a held object. Two cutouts in the initial composite set came from test photographs, so we discarded that experiment, rebuilt the set with training-split cutouts, and reran all four seeds. The corrected paired difference was +0.034 [-0.063, 0.199], which does not support a reliable hand-composite effect. Single-seed transfer experiments produced source-dependent rankings between joint mixing and sequential pretraining. None of the evaluated configurations exceeded the real-only baseline. The reported intervals quantify seed variation; the 31-photo test set remains too small for strong class-specific conclusions.

发表机构

• University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）

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