Research on object detection in autonomous driving road scene based on improved YOLOv11 algorithm
Revolutionizing Autonomous Driving Object Detection
This study enhances the YOLOv11 algorithm for road scene object detection using the Udacity Self-Driving Car dataset. Proposed improvements include an additional detection layer for small targets, replacement of the C3k2 module with an RCS-OSA module for enhanced feature extraction, and implementation of the PAA attention mechanism for optimized feature fusion. Experimental results show significant performance improvements, with a 4.6% increase in mAP@0.5, 7.6% gain in mAP 50-95, and 6.4% enhancement in recall rate compared to the baseline. These modifications effectively address key challenges in detecting distant small targets and dense object clusters within complex driving environments.
Executive Impact & Key Performance Uplifts
Achieve significantly improved object detection accuracy for autonomous driving road scenes, particularly for challenging small and occluded targets, while maintaining real-time performance.
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mAP@0.5 Increase
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mAP 50-95 Gain
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Recall Rate Enhancement
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Real-time Inference
(Meets real-time needs of 5-15 FPS for complex scenarios)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Breakthroughs in Object Detection for Autonomous Systems
This section explores the core innovations and their practical implications for enhancing object detection in complex autonomous driving environments. Our analysis focuses on how these advancements contribute to greater reliability and safety.
4.6%
Increase in mAP@0.5
The enhanced YOLOv11 algorithm demonstrates a 4.6% increase in mean average precision at IoU=0.5, significantly improving baseline detection accuracy for autonomous driving road scenes, particularly for challenging small and occluded targets.
Enterprise Process Flow
Backbone Network
→
RCS-OSA Module
→
PAA Attention Mechanism
→
Additional Detection Layer
→
Enhanced Head for Prediction
The improved YOLOv11 architecture integrates advanced modules for superior feature extraction and fusion, culminating in an optimized detection head for autonomous driving scenarios.
| Algorithm | Key Enhancements/Strengths | Performance (mAP@0.5) | FPS |
|---|---|---|---|
| Faster-RCNN |
|
78.6% | 10.7 |
| SSD |
|
52.1% | 21.8 |
| YOLOv5 |
|
86.1% | 55.6 |
| YOLOv11 (Baseline) |
|
84.8% | 63.1 |
| Improved YOLOv11 (Ours) |
|
89.4% | 14.9 |
A comparison of the proposed improved YOLOv11 algorithm against other state-of-the-art methods like Faster-RCNN, SSD, YOLOv5, and baseline YOLOv11, highlighting its superior accuracy in autonomous driving scenarios. |
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Addressing Complex Driving Environment Challenges
Enhanced Object Detection for Autonomous Vehicles
The original YOLOv11 algorithm faced limitations in complex traffic environments, particularly with small and occluded targets. Our proposed enhancements, including the RCS-OSA module for better feature extraction and a PAA attention mechanism for feature fusion, specifically target these challenges. The addition of a specialized detection layer for small objects further strengthens its capability, reducing false positives and missed detections in dense clusters and distant targets, crucial for safety and reliability in autonomous driving systems.
Calculate Your Potential AI-Driven ROI
Estimate the efficiency gains and cost savings your enterprise could realize by implementing advanced AI solutions like improved object detection.
Annual Cost Savings
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Annual Hours Reclaimed
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Your Strategic Implementation Roadmap
A phased approach to integrate improved YOLOv11 for enhanced autonomous driving perception, ensuring a smooth and effective deployment within your existing infrastructure.
Phase 1: Enhanced Feature Extraction with RCS-OSA
Integrate the Reparametrized Convolution based on channel Shuffle and One-Shot Aggregation (RCS-OSA) module to replace the original C3k2 module, enhancing feature extraction capabilities for small and long-distance targets by expanding the receptive field and promoting feature multiplexing.
Phase 2: Optimized Feature Fusion with PAA
Implement the Parallelized Patch-Aware Attention (PAA) mechanism to address key information loss during downsampling operations, utilizing a multi-branch feature extraction strategy and attention mechanisms for adaptive feature enhancement and accurate feature fusion.
Phase 3: Specialized Small Target Detection Layer
Add an additional detection layer and a specialized detection head to the network's neck to effectively capture and process features of tiny targets (4x4 to 8x8 pixels), significantly improving detection accuracy and recall for small objects.
Phase 4: Real-world Dataset Training & Validation
Train and validate the improved YOLOv11 algorithm using the Udacity Self-Driving Car dataset, ensuring robustness and generalization across various road environments, weather conditions, and target densities.
Phase 5: Performance Benchmarking & Refinement
Conduct comprehensive ablation studies and comparative experiments against state-of-the-art models, refine the algorithm based on performance metrics (mAP, recall, FPS), and confirm its suitability for real-time autonomous driving applications.
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