遮挡对于目标检测的影响分析

An analysis of occlusion influence on object detection

当前目标检测任务中遮挡问题是一项具有挑战性的工作,由于存在遮挡导致物体的整体特征结构遭到破坏,在检测过程中容易发生漏检、误检等问题。常见遮挡处理方法在很大程度上提高了遮挡检测效果,然而对遮挡构成因素和不同遮挡比例对于检测性能的影响情况,目前并没有具体量化分析。对此,从数据驱动方法出发,通过仿真方式构建生成大量均匀分布的遮挡数据集(MOCOD),在此数据集上分析不同遮挡比例下的检测性能,量化分析了不同遮挡对于检测性能的影响情况,在分析的基础上,通过按遮挡比例引入衰减权重方式来筛选高质量的正样本参与模型训练,有效提升了遮挡情况下的检测性能。

The occlusion problem poses challenges to the current object detection.The presence of occlusion could destroy the overall structure of the object,which is likely to incur missing detections and false positives during the detection.Although the common methods for handling occlusion have greatly enhanced the performance of occlusion detection,there remains no specific quantitative analysis of the occlusion components and the impact of different occlusion ratios on the detection performance.In this paper,based on the data-driven method,a large number of uniform occlusion datasets were generated by simulation,named as More than Common Object Detection(MOCOD),and the detection performance under different occlusion ratios was analyzed quantitatively.On the basis of the analysis of occlusion’s influence,according to the occlusion ratios,the decay weight was introduced to select high-quality positive samples for the model training,thereby effectively improving the detection performance under occlusion conditions.