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Computer vision‐based fire detection algorithms have been extensively researched over the past decades as they offer reliable and efficient solutions to early fire detection, as well as economical solutions in outdoor environments. The proposed systems have different levels of complexity; nevertheless, they rely heavily on colour based fire segmentation as the first processing stage using different colour spaces. This paper presents a new flame‐based colour space which accentuates the difference between fire and non‐fire pixel values. The hybrid Artificial Bee Colony‐Teaching Learning‐based Optimisation (ABC‐TLBO) and the k‐medoids clustering algorithm along with a Feature image constructed manually from a wide dataset of fire and non‐fire pixels were used to deduce the optimised colour space conversion matrix. The latter can then be used to transform the RGB pixels from a camera feed into the new Flame‐based colour space for a more efficient fire segmentation. The proposed colour space is capable to separate fire and non‐fire pixels into two intensity classes, whereby the resulting between‐class variance is significantly widened; this maximises the effectiveness of the binary thresholding to separate fire from non‐fire pixels. The experimental results demonstrate that the proposed flame‐based colour space provides a superior average qualitative and quantitative performance on benchmark datasets compared to state‐of‐the‐art colour models; a 20% average increase in F1 score demonstrates a better balance between fire detection and false alarm rates. Moreover, the computation time of the fire detection system is faster by 15.6% in average. The proposed system provides a better implementation of the colour‐based fire segmentation processing stage in vision‐based fire detection systems; it vindicates, in this application context, the superiority of flame‐tuned colour space over conventional ones. [ABSTRACT FROM AUTHOR]