An Efficient Wide-Range Pseudo-3D Vehicle Detection Using A Single Camera

• URL: http://arxiv.org/abs/2309.08369v1
• Date: Fri, 15 Sep 2023 12:50:09 GMT
• Title: An Efficient Wide-Range Pseudo-3D Vehicle Detection Using A Single Camera
• Authors: Zhupeng Ye, Yinqi Li, Zejian Yuan
• Abstract summary: This paper proposes a novel wide-range Pseudo-3D Vehicle Detection method based on images from a single camera. To detect pseudo-3D objects, our model adopts specifically designed detection heads. 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.
• Score: 10.573423265001706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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