The structural complexity, material diversity, and defect concealment in industrial detection scenes pose challenges of robustness, multi-information, and effectiveness to optical imaging systems. Partially blurred images due to the limited depth of field (DoF) of industrial imaging systems, shadow occlusions due to simple illumination conditions, and material and texture interference due to multiple compositions have become key issues affecting imaging quality in complex scenes. This paper proposes a systematic scheme fusing the DoF expansion approach, light source optimization, and polarization information (DLP-Fusion) to comprehensively improve imaging quality. Herein, a DoF fusion algorithm and a liquid zoom lens are used to increase the DoF from 2.5 mm to 40 mm. Moreover, a combination of ring light and freely rotatable strip light sources is introduced to improve the uniformity and robustness of the illumination, resulting in an average enhancement of 56.46% in the contrast of the target features. Furthermore, a polarization selection fusion network (PSFNet) is constructed to achieve flare suppression and complex material characterization, with the image naturalness improving by 32.05%. The experimental results with diverse scenes demonstrate that DLP-Fusion considerably improves the DoF range, image uniformity, and target feature contrast. DLP-Fusion exhibits remarkable robustness in various environments and was seamlessly deployed in real-world industrial settings with good performance. This paradigm may open a path toward intelligent imaging systems for sophisticated applications, including multimaterial detection and target recognition under harsh conditions.