提升电池多类别检测精度，解决查询激活不均问题。
Pattern-Enhanced RT-DETR for Multi-Class Battery Detection
- 引入基于模式的动态查询生成，缓解检测器查询不平衡。
- 在6类电池上实现mAP@50达0.782，较基线提升2.8%。
- 适合工业质检与回收场景，兼顾精度与计算效率。
准确高效的电池检测对电子废弃物回收、工业质量控制和自动化分拣系统日益重要。本文构建了首个公开的多类别电池检测基准，涵盖约8,591张标注图像。在相同实验条件下，系统比较了三种基于CNN的检测器（YOLOv8n、YOLOv8s、YOLO11n）和两种基于Transformer的检测器（RT-DETR-L、RT-DETR-X）。在此基础上，提出PaQ-RT-DETR，通过引入基于模式的动态查询生成机制，有效缓解查询激活不平衡问题，且计算开销可忽略。在基线中，YOLO11n以仅2.6M参数达到最高精度（mAP@50: 0.779）；YOLOv8n推理速度最快，达~1,667 FPS。PaQ-RT-DETR-X整体mAP@50达0.782，优于原RT-DETR-X +2.8%，且在全部六类电池（包括数据稀缺的Bike Battery类）上均有稳定提升。研究为工业级电池检测模型选型提供实用指导。
原文摘要 · Abstract (English)
Accurate and efficient battery detection is increasingly important for applications in electronic waste recycling, industrial quality control, and automated sorting systems. In this paper, we present both a comprehensive benchmark and a novel method for multi-class battery detection. We systematically compare three CNN-based detectors (YOLOv8n, YOLOv8s, YOLO11n) and two transformer-based detectors (RT-DETR-L, RT-DETR-X) on a publicly available dataset of approximately 8,591 annotated images under identical experimental conditions, and further propose PaQ-RT-DETR, which introduces pattern-based dynamic query generation into RT-DETR to alleviate query activation imbalance with negligible computational overhead. Among baselines, YOLO11n achieves the best CNN-based accuracy (mAP@50: 0.779) at only 2.6M parameters, while YOLOv8n delivers the fastest inference at ~1,667 FPS. PaQ-RT-DETR-X achieves the highest overall mAP@50 of 0.782, surpassing RT-DETR-X by +2.8% with consistent per-class gains across all six battery categories including the data-scarce Bike Battery class. Our findings provide practical guidance for selecting object detection models in battery-related industrial applications.
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