KD-SegNet: Efficient Semantic Segmentation Network with Knowledge Distillation Based on Monocular Camera  

作　　者：Thai-Viet Dang Nhu-Nghia Bui Phan Xuan Tan 

机构地区：[1]School of Mechanical Engieering,Hanoi University of Science and Technology,Hanoi,10000,Vietnam [2]College of Engineering,Shibaura Institute of Technology,Tokyo,135-8548,Japan

出　　处：《Computers, Materials & Continua》2025年第2期2001-2026,共26页计算机、材料和连续体(英文)

基　　金：funded by Hanoi University of Science and Technology(HUST)under project number T2023-PC-008.

摘　　要：Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid pooling blocks are utilized to decode multi-scale feature maps, creating a complete semantic map inference. The student model not only preserves the strong segmentation performance from the teacher model but also improves the inference speed of the prediction results. The proposed method exhibits a clear advantage over conventional convolutional neural network (CNN) models, as evident from the conducted experiments. Furthermore, the proposed model also shows remarkable improvement in processing speed when compared with light-weight models such as MobileNetV2 and EfficientNet based on latency and throughput parameters. The proposed KD-SegNet model obtains an accuracy of 96.3% and a mIoU (mean Intersection over Union) of 77%, outperforming the performance of existing models by more than 15% on the same training dataset. The suggested method has an average training time that is only 0.51 times less than same field models, while still achieving comparable segmentation performance. Hence, the semantic segmentation frames are collected, forming the motion trajectory for the system in the environment. Overall, this architecture shows great promise for the development of knowledge-based systems for MR’s navigation.

关 键 词：Mobile robot navigation semantic segmentation knowledge distillation pyramid scene parsing fully convolutional networks 

分 类 号：TN9[电子电信—信息与通信工程]