RDE-YOLOv7: An Improved Model Based on YOLOv7 for Better Performance in Detecting Dragon Fruits

Abstract

There is a great demand for dragon fruit in China and Southeast Asia. Manual picking of dragon fruit requires a lot of labor. It is imperative to study the dragon fruit-picking robot. The visual guidance system is an important part of a picking robot. To realize the automatic picking of dragon fruit, this paper proposes a detection method of dragon fruit based on RDE-YOLOv7 to identify and locate dragon fruit more accurately. RepGhost and decoupled head are introduced into YOLOv7 to better extract features and better predict results. In addition, multiple ECA blocks are introduced into various locations of the network to extract effective information from a large amount of information. The experimental results show that the RDE-YOLOv7 improves the precision, recall, and mean average precision by 5.0%, 2.1%, and 1.6%. The RDE-YOLOv7 also has high accuracy for fruit detection under different lighting conditions and different blur degrees. Using the RDE-YOLOv7, we build a dragon fruit picking system and conduct positioning and picking experiments. The spatial positioning error of the system is only 2.51 mm, 2.43 mm, and 1.84 mm. The picking experiments indicate that the RDE-YOLOv7 can accurately detect dragon fruits, theoretically supporting the development of dragon fruit-picking robots.