Improving Hornet Detection with the YOLOv7-Tiny Model:A Case Study on Asian Hornets  

作　　者：Yung-Hsiang Hung Chuen-Kai Fan Wen-Pai Wang 

机构地区：[1]Department of Industrial Engineering&Management,National Chin-Yi University of Technology,Taichung,411030,Taiwan,China

出　　处：《Computers, Materials & Continua》2025年第5期2323-2349,共27页计算机、材料和连续体(英文)

摘　　要：Bees play a crucial role in the global food chain,pollinating over 75% of food and producing valuable products such as bee pollen,propolis,and royal jelly.However,theAsian hornet poses a serious threat to bee populations by preying on them and disrupting agricultural ecosystems.To address this issue,this study developed a modified YOLOv7tiny(You Only Look Once)model for efficient hornet detection.The model incorporated space-to-depth(SPD)and squeeze-and-excitation(SE)attention mechanisms and involved detailed annotation of the hornet’s head and full body,significantly enhancing the detection of small objects.The Taguchi method was also used to optimize the training parameters,resulting in optimal performance.Data for this study were collected from the Roboflow platformusing a 640×640 resolution dataset.The YOLOv7tinymodel was trained on this dataset.After optimizing the training parameters using the Taguchi method,significant improvements were observed in accuracy,precision,recall,F1 score,andmean average precision(mAP)for hornet detection.Without the hornet head label,incorporating the SPD attentionmechanism resulted in a peakmAP of 98.7%,representing an 8.58%increase over the original YOLOv7tiny.By including the hornet head label and applying the SPD attention mechanism and Soft-CIOU loss function,themAP was further enhanced to 97.3%,a 7.04% increase over the original YOLOv7tiny.Furthermore,the Soft-CIOU Loss function contributed to additional performance enhancements during the validation phase.

关 键 词：Computer vision object detection YOLOv7tiny SE SPD Asian hornet 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]