Abstract
Multi-task networks have found widespread applications in the field of autonomous driving, particularly as perception tasks within multi-task learning continue to gain traction. We propose a novel multi-task network aimed at completing several tasks, including object detection, drivable area detection, lane detection, and height-width restriction detection. On the BDD100K dataset, our model achieves Recall at 94.1% for object detection and IoU at 27.7% for lane detection. Model introduces new task heads into a multi-task network using keypoint detection to address the height-width restriction detection. It demonstrates performance improvements compared to previous networks.
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Acknowledgments
Thank Mr. Qian Long, Mr. Jiangtao Peng and Mr. Qiwei Xie for their guidance in the research process, Mr. Xinyi Yang for his help. This study was funded by Beijing Smart Eye Technology Co. Ltd.
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Jiatian Li has received research grants from Beijing Smart Eye Technology Co. Ltd.
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Li, J., Peng, J., Meng, R., Long, Q., Luo, X. (2024). A New Multi-task Network for Autonomous Driving: Efficientnetv1_Unet. In: Huang, DS., Pan, Y., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14872. Springer, Singapore. https://doi.org/10.1007/978-981-97-5612-4_38
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