SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines

Image segmentation plays a crucial role in the roadwork operations of autonomous line-painting machines. However, the limited resources of mobile platforms in intelligent line-painting applications pose a dual challenge of ensuring both accuracy and real-time performance in road segmentation. To address this issue, this study introduces a lightweight yet efficient image segmentation model, termed the SLTM Network. Central to this network is the lightweight SLTM module, which significantly reduces the model’s parameter count and lowers the computational overhead of the decoder. To enhance the interplay of information at different spatial resolutions, the network incorporates an SE attention-enhanced upsampling module (SAUM) and employs a Spatial Attention Sequence (SAS) unit to improve global environment perception at a low computational cost. Comprehensive experimental evaluations on the Cityscapes dataset demonstrate that the SLTM Network excels in balancing speed and accuracy, achieving an mIoU of 70.5% with only 4.07M parameters and an impressive inference speed of 267.1 FPS. On the embedded device Jetson Xavier NX, it achieves an inference speed of 34.2 FPS. Compared to existing lightweight image segmentation models, the SLTM Network exhibits significant advantages in both processing speed and accuracy, making it particularly suitable for real-time autonomous line-painting machine applications.