In response to the current problems of dimensional measurement of workpieces at the stage of accurate extraction of key measurement points of the workpieces, this paper proposes a workpiece target detection method based on the fusion of polarization features and intensity information. The polarization features are introduced on the basis of the workpiece intensity image, and a dual-stream network model with differentiated and efficient interaction between intensity information and polarization features is established to achieve a more efficient fusion of polarization features and intensity information. To validate the algorithm′s effectiveness, we have established a dataset for detecting the saliency of workpiece targets in polarization images. On this dataset, the proposed algorithm outperforms comparison algorithms in terms of Precision, max-F, S-measure, and visual results, underscoring its exceptional performance in workpiece target detection and its outstanding results in workpiece target detection.