This paper presents an automated approach for the assessment of corrosion in reinforcement bars utilizing advanced image processing algorithms in response to the limitation of traditional method of corrosion assessment. The proposed methodology involves acquiring high-resolution images, followed by pre-processing steps such as image enhancement and segmentation to isolate the rebar. A total of 20 reinforcement bars were taken for the study having various percentages of corrosion. Subsequently, tailored image processing algorithms are employed to detect and quantify corrosion. Validation of the proposed automated approach is performed through comparative analyses with manual inspection results and conventional corrosion assessment methods such as half-cell potential testing. The findings demonstrate promising results, showcasing the efficacy and accuracy of the automated image processing technique in detecting the corrosion. The results obtained through image processing showed an accuracy of almost 89% when comparing with the findings of half-cell potentiometer testing.