Abstract <p>An information-analytical system (IAS) is presented for high-speed grayscale image segmentation using a modified defuzzification method with triangular membership functions. The aim of this study is to analyze the effects of simplifying the defuzzification formula on the accuracy and contrast of object delineation. The proposed approach includes adaptive learning of the weight coefficient, enabling a dynamic adjustment of the defuzzification process, depending on target values. The basic method of averaging membership values and a modified version that accounts for nonlinear weights are compared. Experiments conducted on 1024 × 720 images demonstrate that the developed IAS provides high segmentation accuracy and improved object contrast with minimal computational costs. The results confirm the superiority of the proposed method over traditional approaches, emphasizing the prospects for applying artificial intelligence to computer vision tasks.</p>