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The accurate recognition of tree trunks is a prerequisite for precision orchard yield estimation. Facing the practical problems of complex orchard environment and large data flow, the existing object detection schemes suffer from key issues such as poor data quality, low timeliness and accuracy, and weak generalization ability. In this paper, an improved YOLOv8 is designed on the basis of data flow screening and enhancement for lightweight jujube tree trunk accurate detection. Firstly, the key frame extraction algorithm was proposed and utilized to efficiently screen the effective data. Secondly, the CLAHE image data enhancement method was proposed and used to enhance the data quality. Finally, the backbone of the YOLOv8 model was replaced with a GhostNetv2 structure for lightweight transformation, also introducing the improved CAH attention mechanism. Extensive comparison and ablation results show that the average precision of the quality-enhanced dataset over that of the original dataset increases from 81. 2% to 90. 1%, and the YOLOv8s-GhostNetv2-CAH model proposed in this paper reduces the model size by 19. 5% compared to that of the YOLOv8s base model, with precision increasing by 2. 4% to 92. 3%, recall increasing by 1. 4%, mAP@0. 5 increasing by 1. 8%, and FPS being 17. 1% faster.
Ling et al. (Fri,) studied this question.