It is (probably) worth the investment of your time to "make friends" with the Ornstein-Uhlenbeck (OU) process. I suggest getting comfortable converting the parameters between the OU process and it's discrete-time counterpart, the AR(1) process. Then you can simulate and fit with the AR(1). It should also be easy to compute the formula for half-life and see why it contains only the $\theta$ parameter.
I think that there reason that the OU-process comes up so often when we talk about mean-reversion is that it is the simplest process that has a constant half-life. So we can talk about the half-life of the process. That is to say, the half-life of the process
$$
dX_t = -\theta \left(X_t - \mu\right) dt + \sigma dW_t
$$
with $\theta > 0$ and $\sigma > 0$ is
$$
h = \frac{\log \left( 2 \right)}{\theta}
$$
What's interesting here is that $h$ depends only on the (constant) parameters of the model: $\theta$, $\mu$ and $\sigma$. In fact, it depends only on $\theta$. But what is important, is that it does not depend on $X_t$ or $t$. That is to say, it doesn't matter how near or far you currently are from the mean nor how much time has passed so far, the amount of time it will take to get half-way back to the mean is constant.
Contrast this with, for example, the Cox-Ingersoll-Ross model of interest rates which clearly exhibits mean reversion but does not have a constant half life. As far as I can tell, nobody has even bothered to compute the half-life of the CIR process, presumably because it would be some complicated expression involving the current state.
You are trying to avoid all this formality but your crossing-times estimation approach is still assuming something about the model. Suppose that your data really was from an OU process. Crossing-times would be a poor choice because your data is likely to include a period where the process is close to the mean and therefore crosses it back and forth many times in quick succession. If you are trying to infer something simply from the number of observed crossings of the mean then your inference will be very sensitive to how accurately you estimate the mean and to the frequency of the available data. It would only really make sense if your process had a lot of momentum around the mean and therefore cleanly crossed it once per cycle.
Judging by this and your other question, it sounds like you have mental model of your process that including some kind of periodic function (e.g., cosine). You would need different math to try to fit a model like that but those models rarely work in finance. You might look at, say, interest rates and think "clearly there are some cycles here and cosine a periodic/cyclic function" but the length of the cycles varies and any periodic function is likely to have a fixed period.
Two other approaches that you might consider in place of periodic functions are the Schwartz-Smith and fractional-OU processes.
The Schwartz-Smith model has two state variables: a long-term mean that evolves according to a (non-mean reverting, low volatility) Brownian motion and a short-term deviation from that mean that evolves according to a (mean reverting, high volatility) OU process.
Fractional Brownian motion exhibits some long-memory properties and can have paths that look more reasonable for some financial time-series, such as interest rates.
Note that both Schwartz-Smith and fractional-OU processes are extensions or generalizations of the OU process, which reinforces my comment about making friends with the OU process.
One final note: I wrote the SDE of the OU-process with a $-\theta$ at the front to make it clearer that if $\mu = 0$ then it becomes $dX_t = -\theta X_t + \sigma dW_t$. Wikipedia does it the other way around but they are the same parameterization.