To address the challenges of low recognition accuracy for small target defects and insufficient multi-scale feature capture capability in traditional vision-based methods for solar cell inspection, this study proposes an improved YOLOv8 algorithm based on cross-scale feature enhancement and dynamic parameter optimization. First, a multi-branch residual structure is designed as the core, integrating re-parameterization techniques and adjustable dilated convolution to construct the dilation-enhanced reparametrized residual block, which enhances contextual awareness of target defects through cross-layer feature interaction, thereby improving detection accuracy. Second, deformable convolutional networks version 2 are embedded into the C2f module, combined with an auxiliary detection module to build a dynamic feature adaptation network, improving geometric feature extraction for micro-defects. Finally, a wise intersection over union loss function with a dynamic focusing mechanism is introduced to optimize bounding box matching and enhance regression precision. Experimental results demonstrate that the improved model achieves a mean average precision of 91.8% with only 3.03 M parameters, outperforming the baseline model by 4% while maintaining lightweight architecture and improved detection performance.