HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection

• URL: http://arxiv.org/abs/2503.07371v1
• Date: Mon, 10 Mar 2025 14:29:12 GMT
• Title: HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection
• Authors: Qizhi Zheng, Zhongze Luo, Meiyan Guo, Xinzhu Wang, Renqimuge Wu, Qiu Meng, Guanghui Dong,
• Abstract summary: This study proposes a model called HGO-YOLO, which integrates the HGNetv2 architecture into YOLOv8.<n> 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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