Object Segmentation
A segmented model for automobile appearance detection based on improved YOLOv11-seg
This paper focuses on instance segmentation for identifying and delineating individual automobile components. This task is crucial for advanced driver-assistance systems (ADAS), autonomous driving, and automotive inspection due to its requirement for precise spatial awareness of vehicle parts like body panels, lights, and mirrors, often under challenging conditions.
Optimized YOLOv11-seg for Automobile Component Detection
Conventional vehicle appearance detection models struggle with small, irregularly shaped targets, multi-scale feature fusion, and precise boundary segmentation in complex automotive scenes, leading to false negatives and inaccurate localizations. This research introduces an improved YOLOv11-seg framework incorporating two key innovations: the MCALayerPlus attention mechanism for enhanced multi-scale feature extraction and adaptive fusion, and an optimized ShapeIoU loss function for precise geometric and scale-aware segmentation. The MCALayerPlus module integrates MCAGate, MCALayer, and Multi-Level Task Attention (MLTA) to process targets across scales and suppress noise. The ShapeIoU loss refines bounding box regression by considering geometric relationships and target size sensitivity. The proposed model achieves state-of-the-art performance on a specialized automotive dataset, with a mAP@0.5 of 94.09% and mAP@0.5:0.95 of 77.12%, while maintaining a lightweight profile (5.75 MB) and high-speed inference (45.3 FPS). This represents a significant advancement for real-time deployment in intelligent transportation systems, offering superior accuracy, stability, and reliability for fine-grained automotive component analysis under diverse operating conditions.
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mAP@0.5
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mAP@0.5:0.95
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94.09%
State-of-the-Art mAP@0.5 Achieved
Enterprise Process Flow
Input Image
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YOLOv11-seg Backbone (C2K2 blocks)
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MCALayerPlus (Enhanced Feature Extraction)
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Neck (PAN + C3K2 for Multi-scale Fusion)
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MCALayerPlus (Adaptive Feature Weighting)
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Segmentation Heads (P3, P4, P5)
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Improved ShapeIoU Loss Function
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Automobile Component Detections
| Model | mAP@0.5 | mAP@0.5:0.95 | Precision | Recall | GFLOPs | Model Size (MB) | FPS |
|---|---|---|---|---|---|---|---|
| YOLOv11-seg (Baseline) | 92.93% | 75.28% | 90.41% | 88.65% | 8.9 | 5.72 | 48.2 |
| YOLOv5-seg | 92.35% | 73.79% | 87.22% | 89.65% | 9.1 | 55.8 | |
| YOLOv8-seg | 92.84% | 74.71% | 86.98% | 90.32% | 9.3 | 46.3 | |
| Mask R-CNN (ResNet50) | 89.56% | 68.30% | 84.25% | 83.73% | 185.6 | 45.80 | 18.6 |
| Mask2Former (Swin-T) | 93.20% | 76.50% | 89.85% | 89.24% | 210.4 | 82.3 | 12.4 |
| Ours (Improved YOLOv11-seg) | 94.09% | 77.12% | 91.31% | 90.75% | 10 | 5.75 | 45.3 |
Enhanced Small Target Detection in Complex Scenes
The MCALayerPlus attention mechanism, with its multi-channel and multi-level task attention, significantly improves the model's ability to detect small and irregularly shaped automotive components such as emblems, sensors, and intricate lighting elements. In real-world scenarios, this leads to fewer false negatives and more precise localizations compared to baseline models, especially under varying illumination and partial occlusions. The adaptive feature weighting ensures that critical details are not diluted during multi-scale fusion.
Key Benefit: Increased accuracy and robustness for fine-grained component detection.
Metrics Improved: Precision, Recall, mAP@0.5:0.95
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