Small-Target Detection Algorithm Based on STDA-YOLOv8
Due to the inherent limitations of detection networks and the imbalance in training data, small-target detection has always been a challenging issue in the field of target detection. To address the issues of false positives and missed detections in small-target detection scenarios, a new algorithm b...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/9/2861 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849312761859801088 |
|---|---|
| author | Cun Li Shuhai Jiang Xunan Cao |
| author_facet | Cun Li Shuhai Jiang Xunan Cao |
| author_sort | Cun Li |
| collection | DOAJ |
| description | Due to the inherent limitations of detection networks and the imbalance in training data, small-target detection has always been a challenging issue in the field of target detection. To address the issues of false positives and missed detections in small-target detection scenarios, a new algorithm based on STDA-YOLOv8 is proposed for small-target detection. A novel network architecture for small-target detection is designed, incorporating a Contextual Augmentation Module (CAM) and a Feature Refinement Module (FRM) to enhance the detection performance for small targets. The CAM introduces multi-scale dilated convolutions, where convolutional kernels with different dilation rates capture contextual information from various receptive fields, enabling more accurate extraction of small-target features. The FRM performs adaptive feature fusion in both channel and spatial dimensions, significantly improving the detection precision for small targets. Addressing the issue of a significant disparity in the number of annotations between small and larger objects in existing classic public datasets, a new data augmentation method called Copy–Reduce–Paste is introduced. Ablation and comparative experiments conducted on the proposed STDA-YOLOv8 model demonstrate that on the VisDrone dataset, its accuracy improved by 5.3% compared to YOLOv8, reaching 93.5%; on the PASCAL VOC dataset, its accuracy increased by 5.7% compared to YOLOv8, achieving 94.2%, outperforming current mainstream target detection models and small-target detection algorithms like QueryDet, effectively enhancing small-target detection capabilities. |
| format | Article |
| id | doaj-art-ddb5ff3a86a644c59bbf83b6c1111bef |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ddb5ff3a86a644c59bbf83b6c1111bef2025-08-20T03:52:57ZengMDPI AGSensors1424-82202025-04-01259286110.3390/s25092861Small-Target Detection Algorithm Based on STDA-YOLOv8Cun Li0Shuhai Jiang1Xunan Cao2School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaDue to the inherent limitations of detection networks and the imbalance in training data, small-target detection has always been a challenging issue in the field of target detection. To address the issues of false positives and missed detections in small-target detection scenarios, a new algorithm based on STDA-YOLOv8 is proposed for small-target detection. A novel network architecture for small-target detection is designed, incorporating a Contextual Augmentation Module (CAM) and a Feature Refinement Module (FRM) to enhance the detection performance for small targets. The CAM introduces multi-scale dilated convolutions, where convolutional kernels with different dilation rates capture contextual information from various receptive fields, enabling more accurate extraction of small-target features. The FRM performs adaptive feature fusion in both channel and spatial dimensions, significantly improving the detection precision for small targets. Addressing the issue of a significant disparity in the number of annotations between small and larger objects in existing classic public datasets, a new data augmentation method called Copy–Reduce–Paste is introduced. Ablation and comparative experiments conducted on the proposed STDA-YOLOv8 model demonstrate that on the VisDrone dataset, its accuracy improved by 5.3% compared to YOLOv8, reaching 93.5%; on the PASCAL VOC dataset, its accuracy increased by 5.7% compared to YOLOv8, achieving 94.2%, outperforming current mainstream target detection models and small-target detection algorithms like QueryDet, effectively enhancing small-target detection capabilities.https://www.mdpi.com/1424-8220/25/9/2861small-target detectioncontextual augmentationfeature refinementYOLOv8 |
| spellingShingle | Cun Li Shuhai Jiang Xunan Cao Small-Target Detection Algorithm Based on STDA-YOLOv8 Sensors small-target detection contextual augmentation feature refinement YOLOv8 |
| title | Small-Target Detection Algorithm Based on STDA-YOLOv8 |
| title_full | Small-Target Detection Algorithm Based on STDA-YOLOv8 |
| title_fullStr | Small-Target Detection Algorithm Based on STDA-YOLOv8 |
| title_full_unstemmed | Small-Target Detection Algorithm Based on STDA-YOLOv8 |
| title_short | Small-Target Detection Algorithm Based on STDA-YOLOv8 |
| title_sort | small target detection algorithm based on stda yolov8 |
| topic | small-target detection contextual augmentation feature refinement YOLOv8 |
| url | https://www.mdpi.com/1424-8220/25/9/2861 |
| work_keys_str_mv | AT cunli smalltargetdetectionalgorithmbasedonstdayolov8 AT shuhaijiang smalltargetdetectionalgorithmbasedonstdayolov8 AT xunancao smalltargetdetectionalgorithmbasedonstdayolov8 |