文献类型：期刊文献

英文题名：DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanism

作者：Lin, Cong[1];Jiang, Wencheng[1];Zhao, Weiye[1];Zou, Lilan[1];Xue, Zhong[2]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang, Peoples R China;[2]Chinese Acad Trop Agr Sci, South Subtrop Crops Res Inst, Zhanjiang, Peoples R China

年份：2025

卷号：16

外文期刊名：FRONTIERS IN PLANT SCIENCE

收录：SCI-EXPANDED(收录号：WOS:001417933500001)、、Scopus(收录号：2-s2.0-85218233402)、WOS

基金：The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Hainan Province Science and Technology Special Fund (No. ZDYF2023XDNY058), Top Ten Guangdong Province Agricultural Science and Technology Innovation Main Attack Directions "Unveiling and Leading" Project (No. 2022SDZG03), and Central Public-interest Scientific Institution Basal Research Fund (No. 1630062022005), the Stable Supporting Fund of Acoustic Science and Technology Laboratory (JCKYS2024604SSJS00301), the Undergraduate Innovation Team Project of Guangdong Ocean University under Grant CXTD2024011, the Open Fund of Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching (Grant NO. 2023B1212030003), Zhanjiang Non-funded Science and Technology Research Program under Grant 2024B01055.

语种：英文

外文关键词：pineapple detection; UAV; BiFPN; YOLOv8; coordinate attention

外文摘要：With the development of deep learning technology and the widespread application of drones in the agricultural sector, the use of computer vision technology for target detection of pineapples has gradually been recognized as one of the key methods for estimating pineapple yield. When images of pineapple fields are captured by drones, the fruits are often obscured by the pineapple leaf crowns due to their appearance and planting characteristics. Additionally, the background in pineapple fields is relatively complex, and current mainstream target detection algorithms are known to perform poorly in detecting small targets under occlusion conditions in such complex backgrounds. To address these issues, an improved YOLOv8 target detection algorithm, named DPD-YOLO (Dense-Pineapple-Detection YOU Only Look Once), has been proposed for the detection of pineapples in complex environments. The DPD-YOLO model is based on YOLOv8 and introduces the attention mechanism (Coordinate Attention) to enhance the network's ability to extract features of pineapples in complex backgrounds. Furthermore, the small target detection layer has been fused with BiFPN (Bi-directional Feature Pyramid Network) to strengthen the integration of multi-scale features and enrich the extraction of semantic features. At the same time, the original YOLOv8 detection head has been replaced by the RT-DETR detection head, which incorporates Cross-Attention and Self-Attention mechanisms that improve the model's detection accuracy. Additionally, Focaler-IoU has been employed to improve CIoU, allowing the network to focus more on small targets. Finally, high-resolution images of the pineapple fields were captured using drones to create a dataset, and extensive experiments were conducted. The results indicate that, compared to existing mainstream target detection models, the proposed DPD-YOLO demonstrated superior detection performance for pineapples in situations where the background is complex and the targets are occluded. The mAP@0.5 reached 62.0%, representing an improvement of 6.6% over the original YOLOv8 algorithm, Precision increased by 2.7%, Recall improved by 13%, and F1-score rose by 10.3%.