  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.
