TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Driving
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2024
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| author | Che, Quang-Huy Le, Duc-Tri Pham, Minh-Quan Nguyen, Vinh-Tiep Lam, Duc-Khai |
| author_facet | Che, Quang-Huy Le, Duc-Tri Pham, Minh-Quan Nguyen, Vinh-Tiep Lam, Duc-Khai |
| contents | Semantic segmentation is a fundamental perception task in autonomous driving, particularly for identifying drivable areas and lane markings to enable safe navigation. However, most state-of-the-art (SOTA) models are computationally intensive and unsuitable for real-time deployment on resource-constrained embedded devices. In this paper, we introduce TwinLiteNet+, an enhanced multi-task segmentation model designed for real-time drivable area and lane segmentation with high efficiency. TwinLiteNet+ employs a hybrid encoder architecture that integrates stride-based dilated convolutions and depthwise separable dilated convolutions, balancing representational capacity and computational cost. To improve task-specific decoding, we propose two lightweight upsampling modules-Upper Convolution Block (UCB) and Upper Simple Block (USB)-alongside a Partial Class Activation Attention (PCAA) mechanism that enhances segmentation precision. The model is available in four configurations, ranging from the ultra-compact TwinLiteNet+_{Nano} (34K parameters) to the high-performance TwinLiteNet+_{Large} (1.94M parameters). On the BDD100K dataset, TwinLiteNet+_{Large} achieves 92.9% mIoU for drivable area segmentation and 34.2% IoU for lane segmentation-surpassing existing state-of-the-art models while requiring 11x fewer floating-point operations (FLOPs) for computation. Extensive evaluations on embedded devices demonstrate superior inference speed, quantization robustness (INT8/FP16), and energy efficiency, validating TwinLiteNet+ as a compelling solution for real-world autonomous driving systems. Code is available at https://github.com/chequanghuy/TwinLiteNetPlus. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16958 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Driving Che, Quang-Huy Le, Duc-Tri Pham, Minh-Quan Nguyen, Vinh-Tiep Lam, Duc-Khai Computer Vision and Pattern Recognition Semantic segmentation is a fundamental perception task in autonomous driving, particularly for identifying drivable areas and lane markings to enable safe navigation. However, most state-of-the-art (SOTA) models are computationally intensive and unsuitable for real-time deployment on resource-constrained embedded devices. In this paper, we introduce TwinLiteNet+, an enhanced multi-task segmentation model designed for real-time drivable area and lane segmentation with high efficiency. TwinLiteNet+ employs a hybrid encoder architecture that integrates stride-based dilated convolutions and depthwise separable dilated convolutions, balancing representational capacity and computational cost. To improve task-specific decoding, we propose two lightweight upsampling modules-Upper Convolution Block (UCB) and Upper Simple Block (USB)-alongside a Partial Class Activation Attention (PCAA) mechanism that enhances segmentation precision. The model is available in four configurations, ranging from the ultra-compact TwinLiteNet+_{Nano} (34K parameters) to the high-performance TwinLiteNet+_{Large} (1.94M parameters). On the BDD100K dataset, TwinLiteNet+_{Large} achieves 92.9% mIoU for drivable area segmentation and 34.2% IoU for lane segmentation-surpassing existing state-of-the-art models while requiring 11x fewer floating-point operations (FLOPs) for computation. Extensive evaluations on embedded devices demonstrate superior inference speed, quantization robustness (INT8/FP16), and energy efficiency, validating TwinLiteNet+ as a compelling solution for real-world autonomous driving systems. Code is available at https://github.com/chequanghuy/TwinLiteNetPlus. |
| title | TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.16958 |