CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection

• URL: http://arxiv.org/abs/2505.23317v1
• Date: Thu, 29 May 2025 10:23:37 GMT
• Title: CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection
• Authors: Woojin Shin, Donghwa Kang, Byeongyun Park, Brent Byunghoon Kang, Jinkyu Lee, Hyeongboo Baek,
• Abstract summary: CF-DETR is an integrated system featuring a novel coarse-to-fine Transformer architecture and a dedicated real-time scheduling framework NPFP**.<n>It partitions each DETR task into a safety-critical coarse subtask for guaranteed critical object detection within its deadline, and an optional fine subtask for enhanced overall accuracy (R2)<n>Under an NPFP** policy, CF-DETR successfully meets strict timing guarantees for critical operations and achieves significantly higher overall and critical object detection accuracy compared to existing baselines across diverse AV workloads.
• Score: 3.002784388635573
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Detection Transformers (DETR) are increasingly adopted in autonomous vehicle (AV) perception systems due to their superior accuracy over convolutional networks. However, concurrently executing multiple DETR tasks presents significant challenges in meeting firm real-time deadlines (R1) and high accuracy requirements (R2), particularly for safety-critical objects, while navigating the inherent latency-accuracy trade-off under resource constraints. Existing real-time DNN scheduling approaches often treat models generically, failing to leverage Transformer-specific properties for efficient resource allocation. To address these challenges, we propose CF-DETR, an integrated system featuring a novel coarse-to-fine Transformer architecture and a dedicated real-time scheduling framework NPFP**. CF-DETR employs three key strategies (A1: coarse-to-fine inference, A2: selective fine inference, A3: multi-level batch inference) that exploit Transformer properties to dynamically adjust patch granularity and attention scope based on object criticality, aiming to satisfy R2. The NPFP** scheduling framework (A4) orchestrates these adaptive mechanisms A1-A3. It partitions each DETR task into a safety-critical coarse subtask for guaranteed critical object detection within its deadline (ensuring R1), and an optional fine subtask for enhanced overall accuracy (R2), while managing individual and batched execution. Our extensive evaluations on server, GPU-enabled embedded platforms, and actual AV platforms demonstrate that CF-DETR, under an NPFP** policy, successfully meets strict timing guarantees for critical operations and achieves significantly higher overall and critical object detection accuracy compared to existing baselines across diverse AV workloads.

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