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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.07371 |
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| _version_ | 1866908415484231680 |
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| author | Zheng, Qizhi Luo, Zhongze Guo, Meiyan Wang, Xinzhu Wu, Renqimuge Meng, Qiu Dong, Guanghui |
| author_facet | Zheng, Qizhi Luo, Zhongze Guo, Meiyan Wang, Xinzhu Wu, Renqimuge Meng, Qiu Dong, Guanghui |
| contents | Accurate, real-time object detection on resource-constrained hardware is critical for anomaly-behavior monitoring. We introduce HGO-YOLO, a lightweight detector that combines GhostHGNetv2 with an optimized parameter-sharing head (OptiConvDetect) to deliver an outstanding accuracy-efficiency trade-off. By embedding GhostConv into the HGNetv2 backbone with multi-scale residual fusion, the receptive field is enlarged while redundant computation is reduced by 50%. OptiConvDetect shares a partial-convolution layer for the classification and regression branches, cutting detection-head FLOPs by 41% without accuracy loss. On three anomaly datasets (fall, fight, smoke), HGO-YOLO attains 87.4% mAP@0.5 and 81.1% recall at 56 FPS on a single CPU with just 4.3 GFLOPs and 4.6 MB-surpassing YOLOv8n by +3.0% mAP, -51.7% FLOPs, and 1.7* speed. Real-world tests on a Jetson Orin Nano further confirm a stable throughput gain of 42 FPS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07371 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection Zheng, Qizhi Luo, Zhongze Guo, Meiyan Wang, Xinzhu Wu, Renqimuge Meng, Qiu Dong, Guanghui Computer Vision and Pattern Recognition Accurate, real-time object detection on resource-constrained hardware is critical for anomaly-behavior monitoring. We introduce HGO-YOLO, a lightweight detector that combines GhostHGNetv2 with an optimized parameter-sharing head (OptiConvDetect) to deliver an outstanding accuracy-efficiency trade-off. By embedding GhostConv into the HGNetv2 backbone with multi-scale residual fusion, the receptive field is enlarged while redundant computation is reduced by 50%. OptiConvDetect shares a partial-convolution layer for the classification and regression branches, cutting detection-head FLOPs by 41% without accuracy loss. On three anomaly datasets (fall, fight, smoke), HGO-YOLO attains 87.4% mAP@0.5 and 81.1% recall at 56 FPS on a single CPU with just 4.3 GFLOPs and 4.6 MB-surpassing YOLOv8n by +3.0% mAP, -51.7% FLOPs, and 1.7* speed. Real-world tests on a Jetson Orin Nano further confirm a stable throughput gain of 42 FPS. |
| title | HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.07371 |