フレーム事前分類を用いた配達ロボット向け全方位物体検出の高速化
CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification
6台のカメラで360度を撮影する配達ロボットにおいて、フレームを事前に二値分類してから物体検出ネットワークを動かすことで推論を高速化した研究。
著者: Saian Protasov, Pavel Karpyshev, Ivan Kalinov, Pavel Kopanev, Nikita Mikhailovskiy, Alexander Sedunin, Dzmitry Tsetserukou
分類: cs.RO, cs.CV
原文アブストラクト
Mobile autonomous robots include numerous sensors for environment perception. Cameras are an essential tool for robot's localization, navigation, and obstacle avoidance. To process a large flow of data from the sensors, it is necessary to optimize algorithms, or to utilize substantial computational power. In our work, we propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification. An autonomous outdoor mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360-degree field of view was used as the experimental setup. The obtained experimental results revealed that the proposed optimization accelerates the inference time of the neural network in the cases with up to 5 out of 6 cameras containing target objects.
