Jointly network image processing: multi-task image semantic segmentation of indoor scene based on CNN (Retracted article. See vol. 17, pg. 301, 2023)
Jointly network image processing: multi-task image semantic segmentation of indoor scene based on CNN (Retracted article. See vol. 17, pg. 301, 2023)
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DOI：
10.1049/iet-ipr.2020.0088
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发表时间：
2020-12-15
影响因子：
2.3
通讯作者：
Yu, Hui
中科院分区：
文献类型：
--
作者：
Huang, Li;He, Meiling;Yu, Hui
图像语义分割一直是机器人领域的研究热点。其目的是通过对不同的对象进行切分，为对象分配不同的语义类别标签。但在实际应用中，机器人除了需要知道物体的语义类别信息外，还需要知道物体的位置信息，才能完成更复杂的视觉任务。针对复杂的室内环境，设计了一种图像语义分割网络联合目标检测框架。通过在目标检测网络中加入语义分割分支的并行操作，创新性地实现了目标分类、检测和语义分割相结合的多视觉任务。通过设计新的损失函数，利用迁移学习的思想调整训练，最后在自建的室内场景数据集上进行验证，实验证明了本研究的方法是可行有效的，具有良好的鲁棒性。
Image semantic segmentation has always been a research hotspot in the field of robots. Its purpose is to assign different semantic category labels to objects by segmenting different objects. However, in practical applications, in addition to knowing the semantic category information of objects, robots also need to know the position information of objects to complete more complex visual tasks. Aiming at a complex indoor environment, this study designs an image semantic segmentation network framework of joint target detection. Using the parallel operation of adding semantic segmentation branches to the target detection network, it innovatively implements multi-vision task combining object classification, detection and semantic segmentation. By designing a new loss function, adjusting the training using the idea of transfer learning, and finally verifying it on the self-built indoor scene data set, the experiment proves that the method in this study is feasible and effective, and has good robustness.