LF-RTMDet: an instance segmentation algorithm for real-time detection of water-filled barriers - Wang, JiaHao; Wang, Yongqiang; Zhou, Congling; Huang, Jiawei (The Visual computer, 2025)
Water-filled barriers are commonly used traffic control devices, typically employed in road maintenance and regulation scenarios. However, their configuration, formed by interconnected rods, creates a continuous barrier that poses an obstacle for autonomous vehicles. To address this, we selected the RTMDet instance segmentation model as the baseline and proposed a lightweight, fine-grained RTMDet instance segmentation model, called LF-RTMDet. LF-RTMDet incorporates the GhostConv module for lightweight architecture, adds the CARAFE upsampling module to enhance fine-grained segmentation capabilities, and uses the PSA attention mechanism to improve WFB recognition in complex environments. Experimental results show a significant reduction in both the number of parameters and computational complexity, while inference accuracy is significantly improved. Finally, the LF-RTMDet model, after INT8 quantization, achieves a real-time inference speed of 32 FPS on the Jetson Orin Nano, with minimal accuracy loss, meeting the requirements for real-time detection.