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<p>Efficient feature representation is critical for accurate visual perception in autonomous vehicles. Existing two-stage object detection methods often suffer from feature interference between candidate region generation and classification regression tasks, leading to suboptimal performance in complex scenes. To address this, we propose a Dual-Stage Feature Specialization Network (DSFSN) that decouples feature extraction: MobileNetV3 is employed for lightweight candidate region generation, while ResNet-FPN enhances multi-scale feature fusion for precise classification. Extensive experiments on PASCAL VOC and MS COCO datasets demonstrate state-of-the-art performance, achieving 81.6% mAP (9.3% higher than Faster R-CNN) and 29.3% AP on MS COCO, with a 14.9% improvement in small object detection. Real-world tests under diverse conditions (e.g., rain, night) validate the robustness of our method for autonomous driving applications. This work provides a novel framework for balancing accuracy and efficiency in visual perception systems.</p>

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Dual-stage feature specialization network for robust visual object detection in autonomous vehicles

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月3日

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  • Ze Liu,
  • Junhua Wu,
  • Yingfeng Cai,
  • Hai Wang,
  • Long Chen,
  • Qingchao Liu

摘要

Efficient feature representation is critical for accurate visual perception in autonomous vehicles. Existing two-stage object detection methods often suffer from feature interference between candidate region generation and classification regression tasks, leading to suboptimal performance in complex scenes. To address this, we propose a Dual-Stage Feature Specialization Network (DSFSN) that decouples feature extraction: MobileNetV3 is employed for lightweight candidate region generation, while ResNet-FPN enhances multi-scale feature fusion for precise classification. Extensive experiments on PASCAL VOC and MS COCO datasets demonstrate state-of-the-art performance, achieving 81.6% mAP (9.3% higher than Faster R-CNN) and 29.3% AP on MS COCO, with a 14.9% improvement in small object detection. Real-world tests under diverse conditions (e.g., rain, night) validate the robustness of our method for autonomous driving applications. This work provides a novel framework for balancing accuracy and efficiency in visual perception systems.