Model Adaption Object Detection System for Robot
This addresses object detection challenges for autonomous robots, but appears incremental as it combines existing techniques like meta-learning and transfer learning.
The paper tackles object detection for robot guidance by proposing a model adaptation system that uses multiple object detection networks selected by a meta neural network, with transfer learning and depthwise separable convolutions to handle changing robot views and limited data.
Object detection for robot guidance is a crucial mission for autonomous robots, which has provoked extensive attention for researchers. However, the changing view of robot movement and limited available data hinder the research in this area. To address these matters, we proposed a new vision system for robots, the model adaptation object detection system. Instead of using a single one to solve problems, We made use of different object detection neural networks to guide the robot in accordance with various situations, with the help of a meta neural network to allocate the object detection neural networks. Furthermore, taking advantage of transfer learning technology and depthwise separable convolutions, our model is easy to train and can address small dataset problems.