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WDDR-YOLO: a Driver Distraction Detection Algorithm
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Abstract
En 中文
To address the challenge that deep visual models struggle to attain a well-tuned equilibrium between detection accuracy and inference efficiency in resource-limited operational contexts, this study proposes a frequency-domain dynamic reparameterization model, WDDR-YOLO. This method takes YOLOv11 as the baseline, with core innovations as follows: 1) Haar Wavelet Decomposition (HWD) is adopted to replace spatial downsampling, enabling the reorganization of features into the frequency-domain channel dimension; 2) A frequency-domain energy-aware Reparameterization Convolutional Structure (RCS) is designed, which extracts frequency-domain features through multiple branches during training and dynamically fuses them into a single convolution kernel during inference; 3) A collaborative mechanism between Multi-Local Channel Attention (MLCA) and Dynamic Upsampling (DySample) is constructed, enhancing the ability to retain high-frequency details through frequency-domain weighting and offset constraints. Experimental results for the driver distraction detection task demonstrate that WDDR-YOLO attains an mAP@50 of 87.0% on the State-Farm dataset, a metric that outperforms the baseline by a margin of 3.9 percentage points, alongside a 17.6% cut in model parameters and a rise in inference speed to 81.4 FPS. It further attains a value of 83.5% for mAP@50 on the AUC DDD dataset, which validates the model's generalization capability across heterogeneous scenarios. The proposed frequency-domain dynamic reparameterization paradigm provides an extensible new approach for the design of lightweight visual perception models.
Keywords:
Object Detection
Distracted Driving Detect
Frequency domain analysis
Module coordination