摘要
本文提出了一种利用残差网络构建的多任务网络模型,在仅增加相对少量计算量的条件下,输出道路场景相关的多种类环境感知数据。此外,在仅有约50M参数的情况下,不仅提高了相对单任务模型的精确度,而且相比于多个单任务的效率有大幅度提升。
We have proposed a multi-task network base on deep residual network model, with only a small fractral mount of additional computation, it capable of performing both segmentation and detection task with same input. Hence, with only 50M parameters, both precision and efficiency has being improved over combination of single tasks.
出处
《中国集成电路》
2017年第8期64-71,共8页
China lntegrated Circuit
关键词
环境感知
卷积神经网络
多任务学习
语义分割
物体检测
Visual Perception
Convolutional Neural Network
Muhitask Learning
Semantic Segmentation
ObjectDetection
