Abstract
The detection of road infrared targets is essential for autonomous driving. Different from RGB images, the acquisition of infrared images is unaffected by visible light. However, the signal-to-noise ratio still presents significant challenges. This study demonstrates an improved infrared target detection model for road infrared target detection. An advanced EfficientNet is incorporated to replace the conventional structure and enhance feature extraction. A Dilated-Residual U-Net is also introduced to reduce the noise of infrared images. Meanwhile, the k-means algorithm and data enhancement are implemented to improve the detection performance. The experimental results show that the mean average precision of the proposed model is observed to be 0.89 for the infrared road dataset with an average detection speed of 10.65 s−1.
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