Preface
As the current advanced deep learning target detection algorithm YOLOv8, a large number of tricks have been assembled, but there is still room for improvement and improvement. According to the detection difficulties in specific application scenarios, different improvement methods can be used. The following series of articles will focus on how to improve YOLOv8 in detail. The purpose is to provide meager help and reference for those students engaged in scientific research who need innovation or friends who engage in engineering projects to achieve better results. Since YOLOv8, YOLOv7, and YOLOv5 algorithms have emerged in 2020, a large number of improved papers have emerged. Whether it is 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 pace 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 continue the serial number of the YOLOv5 series improvement. In addition, the improved method can also be applied to other target detection algorithms such as YOLOv5 for improvement. Hope to be helpful to everyone.Link: https://pan.baidu.com/s/1fN07LssywnP_CFDZGPcK7A
Extraction code: private message after following
1. Solve the problem
The method proposed in this paper mainly replaces layer-by-layer stacking with parallel sub-networks. This helps to effectively reduce depth while maintaining high performance. Try to use the proposed method to improve the target detection algorithm and improve the target detection effect.
2. Basic principles
Original link: 2306.15988.pdf (arxiv.org)