TACR-YOLO提升特殊场景下异常行为检测精度与速度
TACR-YOLO: A Real-time Detection Framework for Abnormal Human Behaviors Enhanced with Coordinate and Task-Aware Representations
- 引入坐标注意力、任务感知注意力和强化颈部网络
- 在PABD数据集上达91.92% mAP，实时性强
- 适合安防监控等需要快速精准识别异常的场景
特殊场景下的异常人体行为检测（AHBD）日益重要。尽管基于YOLO的检测方法在实时任务中表现优异，但仍面临小目标、任务冲突和多尺度融合等挑战。为此，我们提出TACR-YOLO，一种新的实时AHBD框架。引入坐标注意力模块增强小目标检测，任务感知注意力模块缓解分类-回归冲突，强化颈部网络实现精细化多尺度融合。同时，采用K-means聚类优化锚框尺寸，并使用DIoU-Loss提升边界框回归性能。本文还构建了包含8,529个样本、涵盖四类行为的人员异常行为检测（PABD）数据集。大量实验表明，TACR-YOLO在PABD数据集上达到91.92% mAP，兼具良好速度与鲁棒性。消融实验证明各模块贡献显著。该工作为特殊场景下的异常行为检测提供了新思路，推动了领域进展。
原文摘要 · Abstract (English)
Abnormal Human Behavior Detection (AHBD) under special scenarios is becoming increasingly crucial. While YOLO-based detection methods excel in real-time tasks, they remain hindered by challenges including small objects, task conflicts, and multi-scale fusion in AHBD. To tackle them, we propose TACR-YOLO, a new real-time framework for AHBD. We introduce a Coordinate Attention Module to enhance small object detection, a Task-Aware Attention Module to deal with classification-regression conflicts, and a Strengthen Neck Network for refined multi-scale fusion, respectively. In addition, we optimize Anchor Box sizes using K-means clustering and deploy DIoU-Loss to improve bounding box regression. The Personnel Anomalous Behavior Detection (PABD) dataset, which includes 8,529 samples across four behavior categories, is also presented. Extensive experimental results indicate that TACR-YOLO achieves 91.92% mAP on PABD, with competitive speed and robustness. Ablation studies highlight the contribution of each improvement. This work provides new insights for abnormal behavior detection under special scenarios, advancing its progress.
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