Real-Time Semantic Segmentation with Optimized RetinaNet Architectures for Embedded Automotive Systems

• URL: http://arxiv.org/abs/2607.22714v1
• Date: Tue, 21 Jul 2026 08:30:00 GMT
• Title: Real-Time Semantic Segmentation with Optimized RetinaNet Architectures for Embedded Automotive Systems
• Abstract summary: This paper presents an optimized semantic segmentation architecture derived from the RetinaNet detection framework.<n> Opt-RetinaSeg replaces the standard ResNet-50 backbone with a hybrid lightweight feature extractor.<n>The proposed model achieves 73.9% mIoU at 70.4 FPS, representing a 7.4x inference speedup and a 4x reduction in model size relative to the ResNet-50 baseline.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Real-time perception is a foundational requirement for advanced driver assistance systems (ADAS) and autonomous vehicles, yet embedded automotive platforms impose severe constraints on compute, memory, and power. This paper presents an optimized semantic segmentation architecture derived from the RetinaNet detection framework, adapted for dense pixel-wise prediction and tailored for deployment on resource-constrained embedded hardware. The proposed architecture, termed Opt-RetinaSeg, replaces the standard ResNet-50 backbone with a hybrid lightweight feature extractor, restructures the Feature Pyramid Network (FPN) to reduce redundant multi-scale computation, and introduces a compact segmentation head guided by focal-loss-inspired class balancing to address the severe foreground-background imbalance common in road scenes. We further apply a three-stage optimization pipeline consisting of structured channel pruning, post-training INT8 quantization, and knowledge distillation from a high-capacity teacher network. Evaluated on the Cityscapes and BDD100K datasets and deployed on an NVIDIA Jetson Xavier NX and a Qualcomm QCS610 automotive SoC, the proposed model achieves 73.9% mIoU at 70.4 FPS, representing a 7.4x inference speedup and a 4x reduction in model size relative to the ResNet-50 baseline, with less than 3% accuracy degradation. These results indicate that RetinaNet-derived architectures, when systematically optimized, are viable candidates for real-time semantic segmentation in embedded automotive perception pipelines

Related papers

• LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation [53.88286427894783]
  Vision Language Models (VLMs) have emerged in the robotic domain as a powerful tool that enables environmental perception with language context.<n>Yet, their computational footprint typically confines them to cloud execution, hindering low-latency inference with local deployment on resource-constrained robots.<n>We present a distillation strategy that transfers complex spatial-semantic reasoning from large frontier models into a lightweight, 4B- parameter local VLM for edge execution on embedded GPU devices.
  arXiv  Detail & Related papers  (2026-06-26T09:11:59Z)
• XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity [9.562751934535154]
  We introduce a training-free selection framework to automatically identify dataset-specific U-Net configurations.<n>XTinyU-Net segmentation achieves accuracy comparable to the heavy nnU-Net baseline with 400x-1600x fewer parameters.
  arXiv  Detail & Related papers  (2026-05-10T16:34:39Z)
• TinyML Enhances CubeSat Mission Capabilities [8.462623779881566]
  The paper presents a Convolutional Neural Networks (ConvNets) model optimization and deployment pipeline for onboard image classification.<n>Our pipeline integrates structured iterative pruning, post-training INT8 quantization, and hardware-aware operator mapping to compress models.<n>We demonstrate an average reduction in RAM usage of 89.55% and Flash memory of 709% for the optimized models.
  arXiv  Detail & Related papers  (2026-03-20T17:51:07Z)
• VeloxNet: Efficient Spatial Gating for Lightweight Embedded Image Classification [1.9903316442426757]
  This paper introduces VeloxNet, a lightweight CNN architecture that replaces SqueezeNet's fire modules with gated multi-layer perceptron blocks for embedded image classification.<n>We evaluate VeloxNet on three aerial image datasets.
  arXiv  Detail & Related papers  (2026-03-19T21:46:20Z)
• PSTNet: Physically-Structured Turbulence Network [0.0]
  Physically-Structured Turbulence Network (PSTNet) is a lightweight architecture that embeds physics directly into its structure.<n>PSTNet achieves a mean miss-distance improvement of +2.8% with a 78% win rate and a statistically significant effect size.
  arXiv  Detail & Related papers  (2026-03-09T04:46:46Z)
• FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks [1.663204995903499]
  FORTRESS (Function-composition Optimized Real-Time Resilient Structural) is a new architecture that balances accuracy and speed by using a special method.<n>Fortress incorporates three key innovations: a systematic depthwise separable convolution framework, adaptive TiKAN integration, and multi-scale attention fusion.<n>The architecture achieves remarkable efficiency gains with 91% parameter reduction (31M to 2.9M), 91% computational complexity reduction (13.7 to 1.17 GFLOPs), and 3x inference speed improvement.
  arXiv  Detail & Related papers  (2025-07-16T23:17:58Z)
• Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [72.26822499434446]
  Auto-Train-Once (ATO) is an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures.
  arXiv  Detail & Related papers  (2024-03-21T02:33:37Z)
• SmoothNets: Optimizing CNN architecture design for differentially private deep learning [69.10072367807095]
  DPSGD requires clipping and noising of per-sample gradients. This introduces a reduction in model utility compared to non-private training. We distilled a new model architecture termed SmoothNet, which is characterised by increased robustness to the challenges of DP-SGD training.
  arXiv  Detail & Related papers  (2022-05-09T07:51:54Z)
• Multi-Exit Semantic Segmentation Networks [78.44441236864057]
  We propose a framework for converting state-of-the-art segmentation models to MESS networks. specially trained CNNs that employ parametrised early exits along their depth to save during inference on easier samples. We co-optimise the number, placement and architecture of the attached segmentation heads, along with the exit policy, to adapt to the device capabilities and application-specific requirements.
  arXiv  Detail & Related papers  (2021-06-07T11:37:03Z)
• Bottleneck Transformers for Visual Recognition [97.16013761605254]
  We present BoTNet, a powerful backbone architecture that incorporates self-attention for vision tasks. We present models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark. We hope our simple and effective approach will serve as a strong baseline for future research in self-attention models for vision.
  arXiv  Detail & Related papers  (2021-01-27T18:55:27Z)
• Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation [54.28963233377946]
  We propose a joint search framework, called AutoRTNet, to automate the design of segmentation strategies. Specifically, we propose hyper-cells to jointly decide the network depth and downsampling strategy, and an aggregation cell to achieve automatic multi-scale feature aggregation.
  arXiv  Detail & Related papers  (2020-03-31T14:02:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.