# Model Adaption Object Detection System for Robot

Source: [Model Adaption Object Detection System for Robot](<https://www.sota2.com/research/paper/68fa66ac1e209131cea1197a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:55:21.281Z

Authors: Jingwen Fu, Licheng Zong, Yinbing Li, Ke Li, Bingqian Yang, Xibei Liu

Published: 2019-11-07T02:20:36.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.02718>)

[PDF](<https://arxiv.org/pdf/1911.02718.pdf>)