Object Detection Based on YOLO Network 论文

摘要

Object detection based on the deep learning has achieved very good performances. However, there are many problems with images in real-world shooting such as noise, blurring and rotating jitter, etc. These problems have an important impact on object detection. Using traffic signs as an example, we established image degradation models which are based on YOLO network and combined traditional image processing methods to simulate the problems existing in real-world shooting. After establishing the different degradation models, we compared the effects of different degradation models on object detection. We used the YOLO network to train a robust model to improve the average precision (AP) of traffic signs detection in real scenes.