合成画像で自動運転車のテストベンチマークを改善する
Improving benchmarks for autonomous vehicles testing using synthetically generated images
データが乏しい国の交通標識に対応するため、少量の国内データセットと合成画像でモデルを更新し、約10%の精度向上を達成した。
分類: cs.LG, cs.CV, cs.RO, stat.ML
原文アブストラクト
Nowadays autonomous technologies are a very heavily explored area and particularly computer vision as the main component of vehicle perception. The quality of the whole vision system based on neural networks relies on the dataset it was trained on. It is extremely difficult to find traffic sign datasets from most of the counties of the world. Meaning autonomous vehicle from the USA will not be able to drive though Lithuania recognizing all road signs on the way. In this paper, we propose a solution on how to update model using a small dataset from the country vehicle will be used in. It is important to mention that is not panacea, rather small upgrade which can boost autonomous car development in countries with limited data access. We achieved about 10 percent quality raise and expect even better results during future experiments.