Article directory
foreword
As the current advanced deep learning target detection algorithm YOLOv7, 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 YOLOv7 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. Due to YOLOv7, YOLOv5 algorithm has emerged a large number of improved papers since 2020. 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 YOLOv5 series improvements. In addition, the improvement method can also be applied to other algorithms such as YOLOv5 for improvement. Hope to be helpful to everyone.
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1. Solve the problem
Try to use the modules in the advanced network to the yolo network to improve the detection effect. The ECVBlock in this network will have a certain effect on improving the detection effect of small targets.