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Abstract
Abstract Headlight is the most explicit and stable image feature in nighttime scenes. This study proposes a headlight detection and pairing algorithm that adapts to numerous scenes to achieve accurate vehicle detection in the nighttime. This algorithm improved the conventional histogram equalization by using the difference before and after the equalization to suppress the ground reflection and noise. Then, headlight detection was completed based on this difference as a feature. In addition, the authors combined coordinate information, moving distance, symmetry, and stable time to implement headlight pairing, thus enabling vehicle detection in the nighttime. This study effectively overcame complex scenes such as high-speed movement, multi-headlight, and rains. Finally, the algorithm was verified by videos of highway scenes; the detection rate was as high as 96.67%. It can be implemented on the Raspberry Pi embedded platform, and its execution speed can reach 25 frames per second.
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