Wide-range and fine-grained vehicle detection plays a critical role in enabling active safety features in intelligent driving systems. However, existing vehicle detection methods based on rectangular bounding boxes (BBox) often struggle with perceiving wide-range objects, especially small objects at long distances. And BBox expression cannot provide detailed geometric shape and pose information of vehicles. This paper proposes a novel wide-range Pseudo-3D Vehicle Detection method based on images from a single camera and incorporates efficient learning methods. This model takes a spliced image as input, which is obtained by combining two sub-window images from a high-resolution image. This image format maximizes the utilization of limited image resolution to retain essential information about wide-range vehicle objects. To detect pseudo-3D objects, our model adopts specifically designed detection heads. These heads simultaneously output extended BBox and Side Projection Line (SPL) representations, which capture vehicle shapes and poses, enabling high-precision detection. To further enhance the performance of detection, a joint constraint loss combining both the object box and SPL is designed during model training, improving the efficiency, stability, and prediction accuracy of the model. Experimental results on our self-built dataset demonstrate that our model achieves favorable performance in wide-range pseudo-3D vehicle detection across multiple evaluation metrics. Our demo video has been placed at https://www.youtube.com/watch?v=1gk1PmsQ5Q8.
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