Lane detection and drivable area segmentation are crucial for safe and efficient navigation on roads. To address the challenges of poor recognition under complex traffic conditions and limited generalization ability in existing lane detection methods, we propose an efficient and lightweight approach for lane and drivable area detection. By leveraging the concept of difference, we introduce a Differential Boundary-Aware Module (DBAM) that enhances lane boundary features and effectively captures the elongated nature of lane markings in complex scenarios. Additionally, we incorporate an Interactive Attention Network (IAN) to learn spatial dependencies between different task features, alleviating potential conflicts. Our method achieves competitive results on the BDD100K dataset, with lane detection Intersection over Union (IoU) reaching 31.3%, and drivable area mean IoU (mIoU) achieving 91.2%, while maintaining a processing speed of 130 FPS. The results demonstrate that the proposed method achieves excellent detection performance in complex scenes.

Zhenkun Hu, Yi Shen. Lane detection based on boundary feature enhancement and information interaction. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 1: 57-63. https://doi.org/10.25236/AJCIS.2025.080108.

[1] Duan K, Bai S, Xie L, et al. Centernet: Keypoint triplets for object detection[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019: 6569-6578.

[2] Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 39(6): 1137-1149.

[3] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263-7271.

[4] Wang C Y, Bochkovskiy A, Liao H Y M. Scaled-yolov4: Scaling cross stage partial network[C]// Proceedings of the IEEE/cvf conference on computer vision and pattern recognition. 2021: 13029-13038.

[5] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023: 7464-7475.

[6] Varghese R, Sambath M. YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness[C]//2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). IEEE, 2024: 1-6.

[7] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3431-3440.

[8] Xie E, Wang W, Yu Z, et al. SegFormer: Simple and efficient design for semantic segmentation with transformers [J]. Advances in neural information processing systems, 2021, 34: 12077-12090.

[9] Huang H, Lin L, Tong R, et al. Unet 3+: A full-scale connected unet for medical image segmentation[C]// ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2020: 1055-1059.

[10] Qian Y, Dolan J M, Yang M. DLT-Net: Joint detection of drivable areas, lane lines, and traffic objects [J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(11): 4670-4679.

[11] Mehta S, Rastegari M, Caspi A, et al. Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation[C]//Proceedings of the european conference on computer vision (ECCV). 2018: 552-568.

[12] Wu D, Liao M W, Zhang W T, et al. Yolop: You only look once for panoptic driving perception[J]. Machine Intelligence Research, 2022, 19(6): 550-562.

[13] Che Q H, Nguyen D P, Pham M Q, et al. TwinLiteNet: An efficient and lightweight model for driveable area and lane segmentation in self-driving cars[C]//2023 International Conference on Multimedia Analysis and Pattern Recognition (MAPR). IEEE, 2023: 1-6.

[14] Tian W, Yu X, Hu H. Interactive attention learning on detection of lane and lane marking on the road by monocular camera image[J]. Sensors, 2023, 23(14): 6545.

[15] Wang H, Qiu M, Cai Y, et al. Sparse u-pdp: A unified multi-task framework for panoptic driving perception [J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(10): 11308-11320.

[16] Wang J, Wu Q M J, Zhang N. You only look at once for real-time and generic multi-task[J]. IEEE Transactions on Vehicular Technology, 2024.

[17] Satzoda R K, Sathyanarayana S, Srikanthan T, et al. Hierarchical additive Hough transform for lane detection [J]. IEEE Embedded Systems Letters, 2010, 2(2): 23-26.

[18] Neven D, De Brabandere B, Georgoulis S, et al. Towards end-to-end lane detection: an instance segmentation approach[C]//2018 IEEE intelligent vehicles symposium (IV). IEEE, 2018: 286-291.

[19] Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510-4520.

[20] Zhang X, Zhou X, Lin M, et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6848-6856.

[21] Zhang Z, Bao L, Xiang S, et al. B2CNet: A Progressive Change Boundary-to-Center Refinement Network for Multi-Temporal Remote Sensing Images Change Detection [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024.

[22] Ma S, Duan S, Hou Z, et al. Multi-object tracking algorithm based on interactive attention network and adaptive trajectory reconnection[J]. Expert Systems with Applications, 2024, 249: 123581.