首页|A Lightweight and Effective YOLO Model for Infrared Small Object Detection
A Lightweight and Effective YOLO Model for Infrared Small Object Detection
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NETL
NSTL
World Scientific
Detecting small targets in infrared images presents significant challenges due to low resolution, lack of texture information, and high noise interference. To address these issues, this paper proposes an improved YOLO model aimed at enhancing the accuracy and efficiency of small target detection in infrared images. First, we integrate the Coordinate Attention (CA) mechanism into the backbone network to improve the model’s feature extraction capability in complex scenes. Second, we introduce the Contextual Feature Aggregation (CFA) module into the neck network, effectively merging multi-level contextual information and enhancing the detection capability for small targets. To further optimize the model, we simplify the detection layers for large targets in YOLO, reducing the number of parameters while maintaining high detection accuracy. Finally, we incorporate the Normalized Wasserstein Distance (NWD) loss function, which is insensitive to target size and can accelerate model convergence while improving small target detection performance. We evaluated the model on public datasets such as VisDrone2019 and FLIR, using metrics like mean Average Precision (mAP) to assess performance. Experimental results indicate that the proposed method achieves higher detection accuracy and efficiency while maintaining a low parameter count compared to baseline models.
Small object detectioninfrared small object detectioncoordinate attentionnormalized Wasserstein distance
Shiyi Wen、Liangfu Li、Wenchao Ren
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School of Computer Science, Shaanxi Normal University, No. 620 West Chang’an Road, Chang’an District Xi’an, Shaanxi,P. R. China
NETL
NSTL
World Scientific
