文献类型：期刊文献

英文题名：SFHG-YOLO: A Simple Real-Time Small-Object-Detection Method for Estimating Pineapple Yield from Unmanned Aerial Vehicles

作者：Yu, Guoyan[1,2,3];Wang, Tao[1];Guo, Guoquan[1];Liu, Haochun[1,2,3]

机构：[1]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Prov Engn Technol Res Ctr Marine Equipme, Zhanjiang 524088, Peoples R China;[3]Southern Lab Marine Sci & Engn Guangdong Prov, Zhanjiang 524013, Peoples R China

年份：2023

卷号：23

期号：22

外文期刊名：SENSORS

收录：SCI-EXPANDED(收录号：WOS:001121011100001)、、EI(收录号：20234815134990)、Scopus(收录号：2-s2.0-85177747874)、WOS

语种：英文

外文关键词：unmanned aerial vehicle; small object detection; lightweight network; adaptive contextual information fusion; high-density object detection; deep learning

外文摘要：The counting of pineapple buds relies on target recognition in estimating pineapple yield using unmanned aerial vehicle (UAV) photography. This research proposes the SFHG-YOLO method, with YOLOv5s as the baseline, to address the practical needs of identifying small objects (pineapple buds) in UAV vision and the drawbacks of existing algorithms in terms of real-time performance and accuracy. Field pineapple buds are small objects that may be detected in high density using a lightweight network model. This model enhances spatial attention and adaptive context information fusion to increase detection accuracy and resilience. To construct the lightweight network model, the first step involves utilizing the coordinate attention module and MobileNetV3. Additionally, to fully leverage feature information across various levels and enhance perception skills for tiny objects, we developed both an enhanced spatial attention module and an adaptive context information fusion module. Experiments were conducted to validate the suggested algorithm's performance in detecting small objects. The SFHG-YOLO model exhibited significant gains in assessment measures, achieving mAP@0.5 and mAP@0.5:0.95 improvements of 7.4% and 31%, respectively, when compared to the baseline model YOLOv5s. Considering the model size and computational cost, the findings underscore the superior performance of the suggested technique in detecting high-density small items. This program offers a reliable detection approach for estimating pineapple yield by accurately identifying minute items.