Preface
As the current advanced deep learning target detection algorithm YOLOv8, it has gathered a large number of tricks, but there is still room for improvement and improvement, aiming at the detection difficulties in specific application scenarios. , can be improved in different ways. The following series of articles will focus on a detailed introduction to how to improve YOLOv8. The purpose is to provide meager help and reference for those students who are engaged in scientific research and need to innovate or those who are engaged in engineering projects who need to achieve better results. Due to the emergence of YOLOv8, YOLOv7, and YOLOv5 algorithms, a large number of improvement papers have emerged since 2020. Whether for students engaged in scientific research or friends who are already working, the value and novelty of the research are not enough. In order to keep up with the times, In the future, the improved algorithm will be based on YOLOv7. The previous YOLOv5 improvement method is also applicable to YOLOv7, so the serial number of the YOLOv5 series of improvements will continue. In addition, the improvement method can also be applied and improved in other target detection algorithms such as YOLOv5. Hope it helps everyone.
1. Solve problems
Change the backbone feature extraction network of the target detection algorithm to the newly proposed RevCol. The features in RevCol will gradually be unraveled as they pass through each column. The original information of these columns will be maintained instead of being compressed or discarded like other networks. Improve detection accuracy.
2. Basic principles
Original link: https://arxiv.org/pdf/2212.11696.pdf
