论文标题
评估小对象检测的切片推断的Yolo模型
Evaluation of YOLO Models with Sliced Inference for Small Object Detection
论文作者
论文摘要
小物体检测在无人机，监视，农业等领域有主要应用。在这项工作中，我们研究了基于YOLO的对象检测模型的性能，用于小型对象检测的任务，因为它们是最流行且易于使用的对象检测模型之一。我们在这项研究中评估了Yolov5和Yolox模型。我们还研究切片的效果有助于推断和细微调整切片的模型有助于推理。我们使用Visdrone2019DET数据集进行培训和评估我们的模型。从大多数对象与图像大小相比，大多数对象相对较小，该数据集具有挑战性。这项工作旨在基于Yolov5和Yolox模型进行小物体检测。我们已经看到，切成薄片的推理在所有实验中都提高了AP50得分，与Yolox模型相比，Yolov5模型的效果更大。切成薄片的微调和切成薄片的推理的效果为所有模型产生了实质性改进。在Visdrone2019DET测试-DEV子集中，Yolov5大型模型实现了最高的AP50分数，得分为48.8。
Small object detection has major applications in the fields of UAVs, surveillance, farming and many others. In this work we investigate the performance of state of the art Yolo based object detection models for the task of small object detection as they are one of the most popular and easy to use object detection models. We evaluated YOLOv5 and YOLOX models in this study. We also investigate the effects of slicing aided inference and fine-tuning the model for slicing aided inference. We used the VisDrone2019Det dataset for training and evaluating our models. This dataset is challenging in the sense that most objects are relatively small compared to the image sizes. This work aims to benchmark the YOLOv5 and YOLOX models for small object detection. We have seen that sliced inference increases the AP50 score in all experiments, this effect was greater for the YOLOv5 models compared to the YOLOX models. The effects of sliced fine-tuning and sliced inference combined produced substantial improvement for all models. The highest AP50 score was achieved by the YOLOv5- Large model on the VisDrone2019Det test-dev subset with the score being 48.8.
