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About this paper
Haocheng Guo, Yaqiong Zhang, Lieyang Chen, Arfat Ahmad Khan
This research presents an improved YOLOv8 network for vehicle detection in autonomous driving, addressing the balance between accuracy, efficiency, and model size. By integrating a FasterNet backbone, a CBAM attention mechanism, and WIoU loss, the model achieves higher detection accuracy for cars, pedestrians, and motorcycles while significantly reducing model size and computational cost.
Key insights
- Replacing the backbone with FasterNet's Partial Convolution (PConv) significantly reduces computational redundancy (FLOPS and memory access) while maintaining feature extraction quality for multi-scale objects.
- Integrating the CBAM attention mechanism (Channel and Spatial Attention) in the neck refines feature representations, helping the model better distinguish objects from complex backgrounds.
- Utilizing the Wise-IoU (WIoU) loss function over CIoU improves bounding box localization and segmentation accuracy by reducing sensitivity to outliers and enhancing focus on intersection quality.
Summary by alphaXiv.
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