Accurate and real-time object detection is crucial for anomaly behavior detection, especially in scenarios constrained by hardware limitations, where balancing accuracy and speed is essential for enhancing detection performance. This study proposes a model called HGO-YOLO, which integrates the HGNetv2 architecture into YOLOv8. This combination expands the receptive field and captures a wider range of features while simplifying model complexity through GhostConv. We introduced a lightweight detection head, OptiConvDetect, which utilizes parameter sharing to construct the detection head effectively. Evaluation results show that the proposed algorithm achieves a mAP@0.5 of 87.4% and a recall rate of 81.1%, with a model size of only 4.6 MB and a frame rate of 56 FPS on the CPU. HGO-YOLO not only improves accuracy by 3.0% but also reduces computational load by 51.69% (from 8.9 GFLOPs to 4.3 GFLOPs), while increasing the frame rate by a factor of 1.7. Additionally, real-time tests were conducted on Raspberry Pi4 and NVIDIA platforms. These results indicate that the HGO-YOLO model demonstrates superior performance in anomaly behavior detection.

翻译：准确且实时的目标检测对于异常行为识别至关重要，尤其在硬件资源受限的场景中，平衡精度与速度是提升检测性能的关键。本研究提出了一种名为HGO-YOLO的模型，它将HGNetv2架构集成到YOLOv8中。这种结合扩展了感受野并捕获了更广泛的特征，同时通过GhostConv简化了模型复杂度。我们引入了一种轻量级检测头OptiConvDetect，它利用参数共享高效构建检测头。评估结果表明，所提算法在CPU上实现了87.4%的mAP@0.5和81.1%的召回率，模型大小仅为4.6 MB，帧率达到56 FPS。HGO-YOLO不仅将精度提升了3.0%，还将计算负载降低了51.69%（从8.9 GFLOPs降至4.3 GFLOPs），同时将帧率提高了1.7倍。此外，我们在树莓派4和NVIDIA平台上进行了实时测试。这些结果表明，HGO-YOLO模型在异常行为检测中展现出优越的性能。