Objective: Walking on irregular terrains is a common situation in everyday life. The accurate detection of gait events is of paramount importance for characterizing and analyzing gait. While several algorithms have been proposed for gait timing estimation on flat terrains, an assessment of their performance on ecological-like terrains is still lacking. The purpose of the present study is to evaluate the performance of several gait event detection algorithms, as proposed in the literature, in the temporal segmentation of gait across different terrains. Methods: Nine healthy volunteers, each mounted with 17 tri-axial inertial sensors, walked on 12 different terrains with varying slopes. Gait events, identified from a marker-based optoelectronic system, were used as the reference. Nine different algorithms were applied to the data, and their performance was analyzed in terms of precision, recall, F1-score, and detection error. The performance scores of the different algorithms were compared across conditions. Results: In general, the results show a decline in performance when transitioning from flat to other terrains, which aligns with expectations as most algorithms are optimized for regular horizontal ground. However, one method (Paraschiv-Ionescu) showed superior performance, achieving near-perfect F1-scores (close to 1) across most conditions. Conclusion: This study compares IMU-based gait event detection algorithms on irregular terrains, revealing that performance degrades as terrain complexity increases. Additionally, this study showcases the ability of the standardized EUROBENCH benchmarking framework to test locomotion in real-like terrain conditions.