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

2025-03-10

AI Summary

To address real-time anomaly behavior detection under hardware-constrained environments, this paper proposes HGO-YOLO—a lightweight and efficient model. It adopts HGNetv2 as the backbone and integrates it with the YOLOv8 framework, introducing a novel Hierarchical-Ghost multi-level feature fusion mechanism and a parameter-sharing lightweight detection head, OptiConvDetect, which significantly enlarges the receptive field while reducing model redundancy. Experimental results show that HGO-YOLO achieves a compact size of 4.6 MB, attains 56 FPS on CPU, and delivers an mAP@0.5 of 87.4% with a recall rate of 81.1%. It reduces computational cost by 51.7% and accelerates inference by 1.7× over the YOLOv8 baseline. The model strikes an optimal trade-off among accuracy, speed, and parameter efficiency, and supports deployment across diverse platforms—including CPUs, Raspberry Pi 4, and NVIDIA GPUs.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Enhances anomaly detection accuracy and speed
• Reduces computational load and model size
• Optimizes real-time performance on limited hardware

Innovation

• Integrates HGNetv2 with YOLOv8 for enhanced features
• Uses GhostConv to simplify model complexity
• Introduces lightweight OptiConvDetect for efficient detection