AI Research Analysis
Research on Real-time Optimization of Intelligent Guide System Integrating Visual Perception and Edge Computing
Authored by Yongzhi Li, Erhua Sun, Baohong Li, Yi Duan | Published: May 09-11, 2025
This paper proposes an intelligent navigation system combining visual perception and edge computing to address challenges in real-time performance and computational resources for visually impaired individuals. Key innovations include a data-driven adaptive preprocessing model (Information Richness Scoring via superpixel segmentation and optical flow), a lightweight multi-task neural network (MT-LiteNet) optimized for edge platforms, and a dynamic offloading framework with end-edge-cloud collaboration. Experimental results show significant improvements: 87 ms average end-to-end latency (5.3x faster than cloud-based), 92.4% obstacle recognition accuracy, and 37% energy consumption reduction. The system offers a safe, reliable, and scalable navigation solution with broad applications beyond visual assistance.
Executive Impact
Key performance indicators demonstrating the system's breakthrough capabilities for real-time applications.
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Average Latency
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Latency Improvement Factor
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Obstacle Recognition Accuracy
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Energy Consumption Reduction
Deep Analysis & Enterprise Applications
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Data-Level Innovations
Algorithmic Contributions
System-Level Innovations
68%
Reduction in Computational Workload
The system achieves a 68% reduction in average computational load by adaptively processing information-rich regions, while maintaining a critical navigation information omission rate below 2%.
Adaptive Preprocessing Flow
Image Partitioning via Superpixels
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Compute Information Richness Score (IRS)
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Classify Regions by IRS
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Adaptive Resource Assignment
Information Richness Scoring (IRS) in Action
Traditional vision systems process entire images uniformly, wasting computation on low-information areas like monotonous walls. Our proposed IRS model, utilizing superpixel segmentation, dynamically analyzes regions based on Clarity, NeighborDiff, and TemporalConsist. Regions with high IRS (e.g., >0.7) undergo full processing, medium IRS (0.3-0.7) get lightweight obstacle detection, and low IRS (<=0.3) skip deep learning inference, reusing prior frames. This intelligent filtering leads to substantial computational efficiency gains without sacrificing crucial navigation data.
Impact: 68% reduction in average computational load, maintaining critical navigation information omission rate below 2%.
1.2
Million Parameters in MT-LiteNet
Our lightweight multi-task neural network, MT-LiteNet, boasts only 1.2 million parameters, making it highly efficient for edge devices.
| Feature | MT-LiteNet (Our System) | Traditional Cloud-based | Traditional Edge-only |
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MT-LiteNet: Balancing Speed and Accuracy
Our MT-LiteNet is a multi-task lightweight network designed for edge devices. Its hybrid backbone combines CNN strengths for local features and Transformer (MobileViT) for global context. A phased multi-task distillation training strategy (pre-training on ImageNet, individual task-specific head training, fine-tuning with adaptive loss weights) ensures robust performance. Mixed-precision quantization (INT8 for feature extraction, FP16 for detection heads, dynamic sparsification for other heads) further reduces model size (1.2MB from 4.7MB FP32) and speeds up inference by 2.3x with only a 1.2% mAP decrease.
Impact: 85 FPS inference speed on edge devices, 1.2MB model size, 2.3x speedup.
87ms
Average End-to-End Latency
The system achieves an average end-to-end latency of 87 ms, representing a 5.3x improvement over traditional cloud-based solutions.
Dynamic Offloading Framework
Input Image Features
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Task Execution Time Forecaster
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Dynamic Task Scheduling Module (Q-learning)
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Dynamic Data Compression
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Execution (Local Edge Node / Cloud)
| Method | Avg Latency (ms) | Timeout Rate (<100ms) | Obstacle mAP@0.5 | Energy Reduction |
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Your AI Implementation Roadmap
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Phase 1: Discovery & Strategy
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Phase 2: Pilot Implementation & Integration
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Phase 3: Optimization & Scalability
Refine the system based on pilot results, optimize the dynamic offloading framework, and scale across your enterprise for maximum efficiency and real-time performance.
Phase 4: Continuous Improvement & Support
Ongoing monitoring, adaptive model updates, and expert support to ensure sustained performance and future-proofing of your AI solutions.
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