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Article

Small-Target Detection Algorithm Based on STDA-YOLOv8

School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2025, 25(9), 2861; https://doi.org/10.3390/s25092861
Submission received: 13 March 2025 / Revised: 17 April 2025 / Accepted: 29 April 2025 / Published: 30 April 2025
(This article belongs to the Section Sensor Networks)

Abstract

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.
Keywords: small-target detection; contextual augmentation; feature refinement; YOLOv8 small-target detection; contextual augmentation; feature refinement; YOLOv8

Share and Cite

MDPI and ACS Style

Li, C.; Jiang, S.; Cao, X. Small-Target Detection Algorithm Based on STDA-YOLOv8. Sensors 2025, 25, 2861. https://doi.org/10.3390/s25092861

AMA Style

Li C, Jiang S, Cao X. Small-Target Detection Algorithm Based on STDA-YOLOv8. Sensors. 2025; 25(9):2861. https://doi.org/10.3390/s25092861

Chicago/Turabian Style

Li, Cun, Shuhai Jiang, and Xunan Cao. 2025. "Small-Target Detection Algorithm Based on STDA-YOLOv8" Sensors 25, no. 9: 2861. https://doi.org/10.3390/s25092861

APA Style

Li, C., Jiang, S., & Cao, X. (2025). Small-Target Detection Algorithm Based on STDA-YOLOv8. Sensors, 25(9), 2861. https://doi.org/10.3390/s25092861

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