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

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