Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection
The application of deep learning in tree pit detection of unmanned aerial vehicle (UAV) images has problems such as dense distribution, high density, small size, false detections, missed detections, and high localization error. To address these challenges, this paper proposes a small target detectio...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525004125 |
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| author | Xiuhao Liang Jun Xiang Sheng Qin Yundan Xiao Lifen Chen Dongxia Zou Honglun Ma Dong Huang Yongxin Huang Wei Wei |
| author_facet | Xiuhao Liang Jun Xiang Sheng Qin Yundan Xiao Lifen Chen Dongxia Zou Honglun Ma Dong Huang Yongxin Huang Wei Wei |
| author_sort | Xiuhao Liang |
| collection | DOAJ |
| description | The application of deep learning in tree pit detection of unmanned aerial vehicle (UAV) images has problems such as dense distribution, high density, small size, false detections, missed detections, and high localization error. To address these challenges, this paper proposes a small target detection algorithm based on SAHI-ImprovedYOLOv8 for detecting tree pits, which includes Slicing Aided Hyper Inference (SAHI), Focal loss, Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (SPPCSPC), and Convolutional Block Attention Module (CBAM). The accuracy of identification and positioning can be improved by using the SAHI via cutting high-resolution UAV imagery into slices that match the detection model, avoiding the loss of small target detail caused by direct downsampling. The identification accuracy is improved by using the Focal loss and CBAM, SPPCSPC to mitigate the data imbalance, strengthen key semantic features, and realize fine-grained information enhancement. The experimental results show that the SAHI-Improved-YOLOv8 model outperforms YOLOv3, YOLOv5, YOLOv8, YOLOv10, YOLOv11 and SAHI-YOLOv8 with a Precision of 85.17 %, a Recall of 85.07 %, a AP50–90 of 78.63 % and a F1 score of 85.12 %. In conclusion, the SAHI-Improved-YOLOv8 has the capability of efficiently processing high-resolution images, which alleviates the problems of high density of small targets, false detections, missed detections, and high localization error. In practical applications, the SAHI-Improved-YOLOv8 model performs excellently in tree pit detection in UAV imagery, significantly reducing false detections and missed detections, and providing reliable technology support for large-scale forest management. |
| format | Article |
| id | doaj-art-dfddf0d40ba7471f902ecc4bf04c5448 |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-dfddf0d40ba7471f902ecc4bf04c54482025-08-20T03:12:53ZengElsevierSmart Agricultural Technology2772-37552025-12-011210118110.1016/j.atech.2025.101181Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detectionXiuhao Liang0Jun Xiang1Sheng Qin2Yundan Xiao3Lifen Chen4Dongxia Zou5Honglun Ma6Dong Huang7Yongxin Huang8Wei Wei9Guangxi Laboratory of Forestry, Guangxi Oil-tea Superior Species Cultivation Research Center of Engineering Technology, Nanning Eucalypt Plantation Ecosystem Observation and Research Station of Guangxi, Guangxi Forestry Research Institute, Nanning 530002, ChinaGuangxi Laboratory of Forestry, Guangxi Oil-tea Superior Species Cultivation Research Center of Engineering Technology, Nanning Eucalypt Plantation Ecosystem Observation and Research Station of Guangxi, Guangxi Forestry Research Institute, Nanning 530002, ChinaGuangxi Key Lab of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin 541000, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaSchool of Physics and Telecommunication Engineering, Yulin Normal University, Yulin 537006, ChinaGuangxi Laboratory of Forestry, Guangxi Oil-tea Superior Species Cultivation Research Center of Engineering Technology, Nanning Eucalypt Plantation Ecosystem Observation and Research Station of Guangxi, Guangxi Forestry Research Institute, Nanning 530002, ChinaGuangxi Zhuang Autonomous Region State-owned Dongmen Forest Farm, Chongzuo 532108, ChinaGuilin Forestry Science Institute, Guilin 541004, ChinaGuangxi State-owned Beijianghe Forest Farm of Rongshui, Liuzhou 545000, ChinaGuangxi Laboratory of Forestry, Guangxi Oil-tea Superior Species Cultivation Research Center of Engineering Technology, Nanning Eucalypt Plantation Ecosystem Observation and Research Station of Guangxi, Guangxi Forestry Research Institute, Nanning 530002, China; Corresponding author.The application of deep learning in tree pit detection of unmanned aerial vehicle (UAV) images has problems such as dense distribution, high density, small size, false detections, missed detections, and high localization error. To address these challenges, this paper proposes a small target detection algorithm based on SAHI-ImprovedYOLOv8 for detecting tree pits, which includes Slicing Aided Hyper Inference (SAHI), Focal loss, Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (SPPCSPC), and Convolutional Block Attention Module (CBAM). The accuracy of identification and positioning can be improved by using the SAHI via cutting high-resolution UAV imagery into slices that match the detection model, avoiding the loss of small target detail caused by direct downsampling. The identification accuracy is improved by using the Focal loss and CBAM, SPPCSPC to mitigate the data imbalance, strengthen key semantic features, and realize fine-grained information enhancement. The experimental results show that the SAHI-Improved-YOLOv8 model outperforms YOLOv3, YOLOv5, YOLOv8, YOLOv10, YOLOv11 and SAHI-YOLOv8 with a Precision of 85.17 %, a Recall of 85.07 %, a AP50–90 of 78.63 % and a F1 score of 85.12 %. In conclusion, the SAHI-Improved-YOLOv8 has the capability of efficiently processing high-resolution images, which alleviates the problems of high density of small targets, false detections, missed detections, and high localization error. In practical applications, the SAHI-Improved-YOLOv8 model performs excellently in tree pit detection in UAV imagery, significantly reducing false detections and missed detections, and providing reliable technology support for large-scale forest management.http://www.sciencedirect.com/science/article/pii/S2772375525004125Tree pit detectionSmall targetSAHI-improved-YOLOv8UAV imageryDeep learning |
| spellingShingle | Xiuhao Liang Jun Xiang Sheng Qin Yundan Xiao Lifen Chen Dongxia Zou Honglun Ma Dong Huang Yongxin Huang Wei Wei Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection Smart Agricultural Technology Tree pit detection Small target SAHI-improved-YOLOv8 UAV imagery Deep learning |
| title | Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection |
| title_full | Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection |
| title_fullStr | Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection |
| title_full_unstemmed | Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection |
| title_short | Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection |
| title_sort | small target detection algorithm based on sahi improved yolov8 for uav imagery a case study of tree pit detection |
| topic | Tree pit detection Small target SAHI-improved-YOLOv8 UAV imagery Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525004125 |
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