<![CDATA[Prevention of accidents for over-height vehicles that will pass through tunnels or underpasses is very necessary. This prevention is done by providing an early warning to over-height vehicles. Early warning is carried out using a system that is able to detect oversized vehicles from the tunnel or underpass height allowed to be passed. This system must have high accuracy and be resistant to various environmental conditions. Interference in detection can occur due to rain, wind, lack of light, night time, various backgrounds such as walls, trees, and two-way roads. This research examines the durability of computer vision-based over-height vehicle detection methods against environmental condition influences. The methods used are Gaussian Mixture Model (GMM) and blob detection. Samples were taken in 3 different location conditions and detected in 5 frame variations. The results of this research show that GMM and blob detection is excellent performance and resilience against environmental conditions. This method can detect over-height vehicles with an accuracy reaching 100% with the number of frames at 5 and 7. It is expected that this detection method can provide more accurate early warnings for over-height vehicles, thereby increasing road user safety.]]>
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