Dust on the surface of photovoltaic (PV) modules significantly reduces their power generation efficiency and may also corrode the protective glass surface of the panels, necessitating monitoring and cleaning. However, existing operational and maintenance detection methods for PV power stations struggle to accurately assess the dust density on PV modules, which brings difficulties to the cleaning work of PV modules. This study proposes an image dark channel prior-based detection method of dust density on PV module surface. First, the relationship between the transmittance of dust on PV module surfaces and the dust density is derived based on the assumption of dust spherical particles. Subsequently, the calculation method for the transmittance of dust on PV module surfaces based on the image bright primary color is presented by the prior knowledge of the dark channel. Finally, an experimental platform is set up and several indoor dust density detection experiments on PV module surfaces are conducted. The experimental results indicate that there exists a clear exponential function relationship between the transmittance calculated using the image dark channel prior knowledge and the dust density. Moreover, the calculated dust density has little relation with the irradiance from light source, and the accuracy of the established model increases first and decreases later with the increase of observation angle and incident angle, which are recommended to take between 30°‒40° for practical application.