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The purpose of this study is to achieve multi-variety and small-batch assembly through direct cooperation between equipment and people and to improve assembly efficiency as well as flexibility.

Firstly, the concept of the human–computer interaction is designed. Secondly, the machine vision technology is studied theoretically. Skin color filter based on hue, saturation and value color model is put forward to screen out images that meet the skin color characteristics of the worker, and a multi-Gaussian weighted model is built to separate moving objects from its background. Both of them are combined to obtain the final images of the target objects. Then, the key technology is applied to the smart assembly workbench. Finally, experiments are conducted to evaluate the role of the human–computer interaction features in improving productivity for the smart assembly workbench.

The result shows that multi-variety and small-batch considerable increases assembly time and the developed human–computer interaction features, including prompting and introduction, effectively decrease assembly time.

This study proves that the machine vision technology studied in this paper can effectively eliminate the interferences of the environment to obtain the target image. By adopting the human–computer interaction features, including prompting and introduction, the efficiency of manual operation is improved greatly, especially for multi-variety and small-batch assembly.