Lithium-ion batteries are critical components in modern energy systems, and their safety and reliability heavily depend on the quality of cell manufacturing. Detecting surface defects on battery cells—such as scratches, pits, wrinkles, and corrosion—is essential yet challenging due to the small size, high diversity, and complex morphology of these defects. Traditional inspection methods, including manual visual checks or conventional 2D imaging, struggle with low accuracy, high false/missed detection rates, and sensitivity to lighting variations in real-world production environments. Moreover, data acquisition under industrial conditions is often hindered by reflective surfaces (e.g., aluminum-plastic film) and inconsistent illumination. To address these issues, this study aims to develop a robust, high-precision defect detection model tailored for structured light imaging, which provides stable, high-contrast 3D-like surface information while mitigating environmental interference.

We propose YOLO-SLB, an enhanced YOLOv8 architecture specifically designed for micro-defect detection on battery cells using structured light images. The model introduces three key innovations. First, the backbone integrates CMUNeXt Blocks featuring large-kernel depthwise convolutions in an inverted bottleneck structure to strengthen spatial feature interaction and preserve fine details critical for detecting minute anomalies. Second, the neck employs a novel C2f_RFAConv module that replaces standard convolutions with receptive field attention convolution (RFAConv), which dynamically generates convolutional weights to overcome the limitations of static weight sharing in large kernels, thereby improving fusion between shallow spatial and deep semantic features. Third, the detection head is redesigned as ASFF_F—a four-branch adaptive spatial feature fusion module that not only applies learnable scale-wise weights but also adds a dedicated micro-target detection path directly utilizing high-resolution shallow features. This reconstruction enhances multi-scale perception and reduces information loss during feature pyramid operations. The model is evaluated on SLBD, a new dataset of 477 original high-resolution (1520 pixel×2000 pixel) battery cell images captured via line-scan cameras under structured light, augmented to 3147 samples covering eight common defect types.

The experimental results show that the proposed model achieves an average detection accuracy of 97.98%, with precision and recall rates of 97.05% and 92.67%, respectively, which are 4.29%, 1.97%, and 6.46% higher than those of the benchmark model YOLOv8. Extensive experiments on the NEU-DET public dataset also verified its good generalization performance, with an average detection accuracy improvement of 0.65% compared to the benchmark model, indicating that this method has high engineering application value.

YOLO-SLB effectively addresses the core challenges of micro-defect detection in lithium battery cells by synergizing structured light imaging with architectural innovations in feature extraction, fusion, and detection. The integration of CMUNeXt, C2f_RFAConv, and ASFF_F significantly boosts sensitivity to small anomalies without compromising robustness. The model's high accuracy, generalization capability, and real-time performance underscore its engineering value for automated quality control in battery manufacturing. Future work will explore semi-supervised learning and online model updating to enable continuous improvement using unlabeled production data.