轻量级中心点检测框架，专为NPU边缘设备优化，精度提升且延迟大幅降低。
OCDet: Object Center Detection via Bounding Box-Aware Heatmap Prediction on Edge Devices with NPUs
- 通过可学习的中心度生成热图，替代固定高斯分布，提升空间定位精度。
- 相比YOLO11，参数减少42%，计算量降34%，NPU延迟降64%，中心对齐得分高23%。
- 适合部署在资源受限的边缘设备，尤其适用于实时监控与工业自动化场景。
边缘设备上的实时目标定位对众多应用至关重要，如监控和工业自动化。传统检测、分割和关键点方法在资源受限环境下表现不佳，常导致目标遗漏。为此，我们提出OCDet，一种专为配备NPU的边缘设备设计的轻量级对象中心检测框架。OCDet通过预测表示对象中心概率的热图，并利用峰值识别提取中心点。不同于以往使用固定高斯分布的方法，我们引入广义中心度（GC），从边界框标注中生成真值热图，无需额外人工标注即可提供更精细的空间细节。基于面向NPU的语义FPN与MobileNetV4主干网络，模型采用平衡连续焦点损失（BCFL）进行训练，缓解数据不平衡问题，聚焦于困难负样本的概率回归任务。结合新的中心对齐评分（CAS）与匈牙利匹配策略，实验表明OCDet在对象中心检测上持续优于YOLO11，CAS最高提升23%，同时参数量减少42%，计算量降低34%，NPU延迟下降64%。与关键点检测框架相比，在相同模型下，OCDet的CAS最高提升达186%。通过集成GC、BCFL与CAS，OCDet建立了一种高效且鲁棒的边缘设备对象中心检测新范式。代码已开源：https://github.com/chen-xin-94/ocdet。
原文摘要 · Abstract (English)
Real-time object localization on edge devices is fundamental for numerous applications, ranging from surveillance to industrial automation. Traditional frameworks, such as object detection, segmentation, and keypoint detection, struggle in resource-constrained environments, often resulting in substantial target omissions. To address these challenges, we introduce OCDet, a lightweight Object Center Detection framework optimized for edge devices with NPUs. OCDet predicts heatmaps representing object center probabilities and extracts center points through peak identification. Unlike prior methods using fixed Gaussian distribution, we introduce Generalized Centerness (GC) to generate ground truth heatmaps from bounding box annotations, providing finer spatial details without additional manual labeling. Built on NPU-friendly Semantic FPN with MobileNetV4 backbones, OCDet models are trained by our Balanced Continuous Focal Loss (BCFL), which alleviates data imbalance and focuses training on hard negative examples for probability regression tasks. Leveraging the novel Center Alignment Score (CAS) with Hungarian matching, we demonstrate that OCDet consistently outperforms YOLO11 in object center detection, achieving up to 23% higher CAS while requiring 42% fewer parameters, 34% less computation, and 64% lower NPU latency. When compared to keypoint detection frameworks, OCDet achieves substantial CAS improvements up to 186% using identical models. By integrating GC, BCFL, and CAS, OCDet establishes a new paradigm for efficient and robust object center detection on edge devices with NPUs. The code is released at https://github.com/chen-xin-94/ocdet.
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