To address the issues encountered in robotic visual acquisition of bolt connection box images in prefabricated building units (such as overall low brightness, uneven illumination on bolt rods, and unclear texture details), this paper proposes an image enhancement algorithm based on logarithmic transformation and multi-scale feature fusion. For the problem of overall dim lighting in bolt connection box images, a logarithmic transformation is adopted and combined with Gamma correction to optimize dynamic range expansion. By adaptively adjusting parameters, the proposed method improves image performance under varying illumination conditions and effectively recovers latent detail information. Furthermore, a multi-scale feature fusion strategy is introduced, in which image information at different scales is integrated through dynamic weight allocation, thereby significantly enhancing texture clarity. Targeted enhancement is applied to the bolt rod region using the Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve local contrast. In addition, edge sharpening and noise suppression techniques are employed to ensure that the enhanced images remain smooth while preserving fine details. Comparative experiments with mainstream image enhancement algorithms demonstrate that the proposed method achieves superior performance in terms of brightness, clarity, and texture detail. Specifically, the average Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are improved by 5.89% and 4.73%, respectively, while the Mean Squared Error (MSE) is reduced by 12.55%.