|.996 Bike Threat|
|.997 Bike Treat|
|Inception v3 looks like this, I'm told|
Not everything was a screaming success, however. I took some low-rez screen grabs from this post that has a clip I shot of one of my more threatening encounters on the road, imagining results that I might get if I had enough hardware and the right software running on the back of my bicycle to alert me of what was about to happen.
The first one scored high as a .997 threat. The second one, taken just before the actual close pass, got a more ambiguous .69 threat rating. The third one, taken just moments before #2, got a much stronger .93 threat rating. When I do this again, I will think more about close passes and real traffic situations, which I had a limited supply of photos of for this first experiment.
What's next for OSGIML? More image classification, I bet. First, more playing around with this experiment, to see what else I can learn.
Add to backlog: the follower-drone should provide data to enhance threat detection, and to attract to bicycle treats on or near the intended navigation route.
|.997 Bike Threat|
|.692 Bike Threat (doh!)|
|.93 Bike Threat (yay!)|
ADDENDUM: Saturday afternoon shots
|bikethreat (score = 0.96360)|
|bikethreat (score = 0.99636)|
|biketreat (score = 0.54084) (confusing shadow is confusing)|
|bikethreat (score = 0.91335)|