山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (4): 83-88.doi: 10.6040/j.issn.1672-3961.0.2021.295
孟令灿,聂秀山*,张雪
MENG Lingcan, NIE Xiushan*, ZHANG Xue
摘要： 由于公交车中场景复杂、干扰因素繁多容易出现遮挡乘客问题,现有深度学习和目标检测方法在对公交车内的拥挤程度分类时精度低、效果差,往往达不到令人满意的效果。针对这一问题,提出一种基于遮挡目标去除的公交车拥挤度分类算法,对公交拥挤进行分类和分析。该方法有遮挡物检测、图像去遮挡和拥挤度分类模块三部分组成。基于目标检测算法检测出遮挡物,通过图像修复算法对乘客图像进行修复,利用拥挤度分类算法分析拥挤度。本研究从真实的公交车中采集数据生成数据集,并进行标注。试验结果表明,基于遮挡目标去除的分类算法的准确率达到了67.12%,与现有的方法对比具有最高的预测精度。
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