Detection of Navel Orange Based on the Improved YOLOv8s in Complex Scenes
The high-precision real-time detection of navel orange is one of the key technologies for intelligent harvesting.We use YOLOv8s to conduct research on the navel orange detection and fur-ther solve the issues of error and missed detections from two aspects:dataset expansion and model im-provement.In terms of dataset expansion,the training samples are expanded by adding fog,enabling the model to recognize navel oranges in areas with poor lighting in the image.In terms of improving the YOLOv8s model,a detection head and a merged block attention module are added to detect navel oran-ges obscured by leaves and other navel oranges.Experimental results show that the improved method a-chieves higher accuracy,recall,and average accuracy than directly using YOLOv8s.
navel orange detectionYOLOv8smerge block attentionexpansion of fogging