In this paper, we propose a glare detection algorithm based on image processing technology for the traditional indoor glare detection method that relies on manual labor, has a single parameter, and has limitations such as low efficiency and poor robustness. Firstly, mean filtering and gray scale transformation are used to suppress image noise, combined with fixed threshold segmentation and morphological optimization to accurately extract the glare region; secondly, a geometric model is established through three-dimensional coordinate mapping, and four types of parameters, namely, stereo angle, position index, glare source brightness and background brightness, are calculated collaboratively. The innovation lies in the design of a highly robust segmentation strategy that combines fixed threshold and morphology to solve the problem of complex background interference, and the proposed multi-parameter synergistic quantization model, which breaks through the bottleneck of traditional uni-dimensional evaluation. Experiments show that the computational error of the algorithm parameters is less than 4%, and this study provides a new scheme for low-cost and high-precision glare detection.