Researchers at Binghamton University are using computer vision and machine-learning to detect landmines from aerial images. Their results reveal the importance of out-of-sample (OOS) testing, which evaluates a model on independently collected images beyond the dataset used to train and tune it.  “It’s easy to get numbers in the 80 or 90 percent range if you’re not doing out-of-sample testing,” said… The post AI landmine detection reveals bigger problems in computer vision testing appeared first on Research & Development World .