日常消毒工作已经成了常态化的工作, 智能消毒机器人是非常有效的一种方式. 机器人通常通过视觉来感知周围环境, 但是基于监督学习的检测算法通常需要大量的标注数据进行训练, 当标注数据量多时, 标注成本非常高, 当标注数据量少时, 模型容易陷入过拟合, 因此少样本目标检测是一种有效的解决途径. 本文以SimDet模型为基础, 提出了SimDet+模型. 第一, 针对消毒场景中的目标检测任务的特点, 增加了自监督预训练的过程, 第二, 因为存在查询图片可供参考, 对分类层进行了改进, 使用余弦相似度代替全连接层来计算置信度, 通过非参数化计算有效避免了过拟合现象. 针对消毒场景, 制作了一份22 min的视频数据集和包含8类物体的检测数据集, 分别用于两个阶段训练. 通过自监督预训练, 有效减少了数据标注成本, 同时下游任务的mAP从
Intelligent disinfection robots are a highly effective way of daily disinfection as it becomes regular. Robots usually perceive the surrounding environment through vision, but object detection based on supervised learning usually requires a large amount of labeled data for training. When the amount of labeled data is large, the cost of labeling is very high, and when the amount of labeled data is small, the model is prone to overfitting. Therefore, few-shot object detection is an effective solution. On the basis of the SimDet Model, this study proposes the SimDet+ model. First, according to the characteristics of the object detection task in a disinfection scene, the process of self-supervised pre-training is added. Second, as there are query images for reference, the classification layer is improved, where the cosine similarity instead of the fully connected layer is employed for confidence level calculation, and thus the overfitting phenomenon is effectively avoided through non-parametric calculation. For the disinfection scene, a 22-minute video dataset and a detection dataset containing eight categories of objects are produced and used in two stages separately for training. Through self-supervised pre-training, the cost of data labeling is effectively reduced, and the mAP of downstream tasks is increased from 0.216 2 to 0.530 2.