As we move toward the release of our beta software, I thought it would be a good time to reflect on our progress so far. As you would see if you cared to review this entire blog, we originally started with the idea that we could measure stream stage (water depth) using a camera. Why was this an attractive method when researchers and government agencies (USGS) already use a number of other methods, such as transducers and bubbler gauges? Well, from our collective experience, we know that field measurements are often erroneous due to instrument drift, infrequent or incorrect instrument calibration, or technician inexperience, just to name a few reasons. We felt the GaugeCam concept could address these error sources, while also providing a way to visually verify measurements.
After completing a brief proof of concept in the laboratory, we deployed a camera in the field near Pullen Park, Raleigh, NC. We chose this approach because we anticipated that the field application would involve many challenges we would never address in a lab-only study. We were correct! Our camera and communications system, which worked beautifully in the lab setting was not as robust in the field as we had hoped. We were able to compile a list of issues associated with our field application, which we have addressed in the beta version of our software. The field application gave us impetus to develop a functional daemon for processing images in real time on the GaugeCam server. Additionally, we were able to gather data for comparison with USGS stream stage data measured at Pullen Park.
While the Pullen Park deployment was underway, we stayed busy in the lab, assessing the capabilities of our camera and software. The camera was tested at a variety of distances and angles relative to the water level bench. We were encourage by the results but knew that to minimize the need for highly experienced technicians, we would need to automate our calibration process. To test the automated calibration, we have modified the water level bench using a white background with horizontal black bars substituting for water level. This was required to reduce the noise introduced by the water meniscus. Once the automatic calibration is verified, we will repeat our earlier study of water level detection from a variety of distance and angles. We will also deploy the system at alpha sites, which have already been identified. The transition from manual calibration to automatic calibration has been a little more difficult than anticipated (as seen in several recent posts). I feel we are encountering a typical challenge for machine vision projects; that the abilities of the human eye are very difficult to emulate using an algorithm!