# LightSeg: Local Spatial Perception Convolution for Real-Time Semantic Segmentation
## 5 Conclusions  
This paper introduces LSPConv, a novel convolutional neural network (CNN) module specifically designed for intelligent recognition and detection on mobile devices. LSPConv effectively addresses the challenge of limited receptive field in lightweight designs by employing spatial separable convolutions with large kernels in the encoder, while also capturing local information using local convolutions. The proposed LSPBlock further enhances the network's receptive field without the drawbacks of dilated convolutions and reduces computational complexity through depthwise separable convolutions.  
By performing spatial separable convolutions channel-wise and fusing features using point-wise convolutions, the proposed approach achieves real-time performance on low-power embedded platforms. This results in a 41% reduction in the number of parameters compared to standard convolution, while increasing inference speed by 30%, thereby improving efficiency for deployment on unmanned systems with limited computational resources.  
Experiments on kernel size combinations reveal that incorporating large kernels in the initial stage using LSPConv does not significantly improve network performance. Therefore, the kernel size is gradually increased in the LSPBlock of stage 3.  
Evaluation of the Cityscapes dataset demonstrates the effectiveness of our model. Compared to DDRNet-23 [36], our model achieves nearly a 13% speedup while sacrificing only 1.7% accuracy compared to RegSeg. These results highlight the efficiency and accuracy achieved by our LSPConv-based approach in real-time semantic segmentation tasks on resource-constrained mobile devices.  
Conceptualization, X.L. and J.L.; methodology, X.L. and J.L.; software, J.L. and Z.G.; validation and J.L.; formal analysis, J.L.; investigation, X.L. and J.L.; resources, X.L. and Z.J.; data curation, J.L. and Z.G.; writing--original draft preparation, J.L.; writing--review and editing, J.L., X.L., and Z.J.; visualization, J.L. and Z.G.; supervision, Z.J.; project administration, Z.J.; funding acquisition, Z.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61876049, 62172118) and Nature Science Key Foundation of Guangxi (2021 GXNSFDA196002); in part by the Sichuan Regional Innovation Cooperation Project (2021 YFO0002); in part by the Guangxi Key Laboratory of Image and Graphic Intelligent Processing under Grants (GIIP2004) and Student's Platform for Innovation and Entrepreneurship Training Program under Grant (S202210595177, 202210595029).  
Not applicable.  
**Data Availability Statement:** For the Cityscape dataset, the data supporting reported results can be found at the following link: [[https://www.cityscapes-dataset.com/](https://www.cityscapes-dataset.com/)]([https://www.cityscapes-dataset.com/](https://www.cityscapes-dataset.com/)) (accessed on 12 May 2023). The dataset is publicly available for research purposes and can be downloaded upon registration on the website. For CamVid dataset, the data supporting reported results can be found at the following link: [[http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)]([http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)) (accessed on 20 June 2023). The dataset is publicly available for research purposes and can be downloaded upon registration on the website.  
The authors declare no conflict of interest.