Object Detection Performance
YOLO v8 Model Comparison
| Model |
mAP@0.5 |
mAP@0.5:0.95 |
Inference Time (ms) |
Model Size (MB) |
FPS (RTX 3080) |
| YOLO v8n |
0.374 |
0.523 |
12.3 |
6.2 |
81.3 |
| YOLO v8s |
0.443 |
0.623 |
23.7 |
21.5 |
42.2 |
| YOLO v8m |
0.487 |
0.677 |
42.1 |
49.7 |
23.8 |
| YOLO v8l |
0.531 |
0.715 |
89.3 |
87.7 |
11.2 |
Best Accuracy
53.1%
YOLO v8l mAP@0.5
Fastest Inference
12.3ms
YOLO v8n
Smallest Model
6.2MB
YOLO v8n
Recommendation: YOLO v8s provides the best balance of accuracy and speed for most autonomous driving applications, achieving 44.3% mAP@0.5 with 42.2 FPS.
Lane Detection Performance
Algorithm Comparison
| Algorithm |
Accuracy (%) |
Processing Time (ms) |
Robustness Score |
Weather Conditions |
| Canny + Hough |
85.2 |
15.3 |
6.2 |
Clear only |
| CNN Segmentation |
94.7 |
67.8 |
8.9 |
All weather |
| Multi-Modal Fusion |
97.8 |
45.2 |
9.4 |
All conditions |
Lane Detection Accuracy vs Processing Time
Hardware Performance
GPU Performance Comparison
| Hardware |
YOLO v8s FPS |
YOLO v8m FPS |
Power Consumption (W) |
Cost Efficiency |
| RTX 4090 |
156.7 |
89.3 |
450 |
High |
| RTX 3080 |
42.2 |
23.8 |
320 |
Best |
| Jetson AGX Orin |
28.5 |
15.2 |
60 |
Excellent |
| Intel NCS2 |
8.7 |
4.2 |
1 |
Good |
Edge Device Performance
| Device |
Inference Time (ms) |
Power (W) |
Memory (GB) |
Temperature (°C) |
| NVIDIA Jetson Xavier NX |
45.2 |
20 |
8 |
65 |
| Intel NUC with OpenVINO |
38.7 |
35 |
16 |
72 |
| Raspberry Pi 4 |
1250.0 |
5 |
4 |
68 |
Dataset Performance
Model Performance Across Datasets
| Dataset |
Images |
Classes |
YOLO v8s mAP |
YOLO v8m mAP |
Best Model |
| COCO |
330K |
80 |
44.3 |
48.7 |
YOLO v8m |
| KITTI |
7.5K |
8 |
78.9 |
82.1 |
YOLO v8m |
| Cityscapes |
25K |
19 |
65.4 |
71.2 |
YOLO v8m |
| Custom Driving |
15K |
12 |
91.7 |
94.2 |
YOLO v8m |
Key Insight: Models perform significantly better on domain-specific datasets like KITTI and custom driving datasets compared to general-purpose datasets like COCO.
Real-time Performance
End-to-End Pipeline Performance
| Pipeline Component |
Processing Time (ms) |
CPU Usage (%) |
Memory (MB) |
Optimization Potential |
| Image Preprocessing |
2.1 |
15 |
45 |
Medium |
| Object Detection |
23.7 |
85 |
890 |
High |
| Lane Detection |
15.3 |
45 |
120 |
Medium |
| Post-processing |
1.8 |
8 |
25 |
Low |
| Total Pipeline |
42.9 |
153 |
1080 |
High |
Target FPS
30
Real-time requirement
Current FPS
23.3
Achieved performance
Optimization Needed
28.7%
Performance improvement
Bottleneck
55.2%
Object detection
Benchmarking Methodology
Testing Environment
- Hardware: NVIDIA RTX 3080, Intel i7-12700K, 32GB DDR4 RAM
- Software: Ubuntu 20.04, CUDA 11.8, PyTorch 1.13, OpenCV 4.6
- Testing Protocol: 1000 inference runs per model, warm-up runs excluded
- Metrics: Mean inference time, standard deviation, 95th percentile
Evaluation Criteria
- Accuracy: mAP@0.5 and mAP@0.5:0.95 on validation datasets
- Speed: Average inference time and FPS measurements
- Efficiency: Power consumption and memory usage
- Robustness: Performance across different weather and lighting conditions
Note: All benchmarks are conducted on standardized test images (640x640 resolution) to ensure fair comparison across different models and hardware configurations.