Collaborative object detection and semantic segmentation algorithm integrating lightweight deep learning for complex traffic scenes
Year: 2026
Type: article
Abstract: Autonomous driving perception requires simultaneous object detection and semantic segmentation under complex traffic conditions while satisfying the memory and latency constraints of edge platforms. To address this requirement, a Lightweight Collaborative Detection and Segmentation Network (LCS-Net) is proposed. The network decouples spatial representation from channel mapping through deeply cascaded lightweight convolutions. It integrates multi-scale context using an improved atrous spatial pyramid pooling module alongside bidirectional gated spatial attention. Furthermore, it balances heterogeneous task convergence through dynamic task weighting and dynamic feature-pixel focal loss. On Cityscapes, LCS-Net achieves 82.53% [email protected] and 76.14% mIoU with 4.19 M parameters, 14.71 G FLOPs, and an inference speed of 91.27 FPS. Compared with YOLOP, it improves [email protected] and mIoU by 6.12 and 5.96 percentage points, respectively, while increasing the inference speed by 34.56%. Compared with YOLOPv2, it reduces the parameter count and FLOPs by 66.02% and 64.36%, respectively, and increases the inference speed by 11.73%. On the BDD100K nighttime subset, LCS-Net maintains 72.19% [email protected], while its peak GPU memory consumption is limited to 1.6 GB. These results indicate that LCS-Net provides an effective accuracy–efficiency trade-off for resource-constrained edge deployment in complex traffic scenes.
Source: Discover Artificial Intelligence
Author: Hongjiang Liu
Institution: Aba Teachers University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7225460359