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<p>Lithium batteries are pivotal in new energy vehicle (NEV) applications. This study proposes a novel defect detection model, Small-object Detection-YOLO (SD-YOLO), based on YOLOv11, to overcome the limitations of conventional methods in detecting small surface defects in lithium batteries, particularly addressing the challenge of high miss rates for low-contrast targets. The Similarity-Aware Activation Module (SimAM) is integrated into the backbone, enabling the model to prioritize small-scale defects and enhance feature extraction. The C3k2 GhostDynamicConv module is introduced to replace conventional convolution layers, enhancing feature representation while reducing computational complexity through dynamic convolutions and Ghost feature integration. To further refine model performance, the Focaler-Iou loss function is employed, replacing the original CIoU loss function, optimizing bounding box regression, and accelerating model convergence. Experimental results show that the proposed model significantly outperforms the original YOLOv11 in key performance metrics, with a 1.3% increase in precision, a 0.5% increase in recall, and a 2.2% increase in mean average precision (mAP). Furthermore, the model outperforms other state-of-the-art methods in both detection speed (FPS) and small-target detection capabilities.</p>

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SD-YOLO: a lightweight model for small target defect detection in complex environments of lithium batteries in new energy vehicles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月18日

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  • Chao Hou,
  • Xiaocheng Tang,
  • Lipeng Ji,
  • Yang Wang,
  • Zhicheng Yin,
  • Renchao Li

摘要

Lithium batteries are pivotal in new energy vehicle (NEV) applications. This study proposes a novel defect detection model, Small-object Detection-YOLO (SD-YOLO), based on YOLOv11, to overcome the limitations of conventional methods in detecting small surface defects in lithium batteries, particularly addressing the challenge of high miss rates for low-contrast targets. The Similarity-Aware Activation Module (SimAM) is integrated into the backbone, enabling the model to prioritize small-scale defects and enhance feature extraction. The C3k2 GhostDynamicConv module is introduced to replace conventional convolution layers, enhancing feature representation while reducing computational complexity through dynamic convolutions and Ghost feature integration. To further refine model performance, the Focaler-Iou loss function is employed, replacing the original CIoU loss function, optimizing bounding box regression, and accelerating model convergence. Experimental results show that the proposed model significantly outperforms the original YOLOv11 in key performance metrics, with a 1.3% increase in precision, a 0.5% increase in recall, and a 2.2% increase in mean average precision (mAP). Furthermore, the model outperforms other state-of-the-art methods in both detection speed (FPS) and small-target detection capabilities.