TS-DETR: A Small Object Detection Model in Autonomous Driving Systems

With the rapid advancement of autonomous driving technology, there is an increasingly urgent demand for faster and more accurate object detection frameworks. Recently, numerous deep learning-based object detectors have shown impressive performance in real-time driving applications. However, the detection of small objects such as various traffic signs remains challenging due to the complex nature of these objects. This paper proposes a transformer-based detector TS-DETR to improve the accuracy of small object detection in autonomous driving systems. We introduce a constrained decoder structure to focus the model's attention on the predicted boxes. Additionally, a content-aware deformable cross-attention mechanism is proposed to obtain more comprehensive attention weights. Experimental results on challenging public datasets such as TT100K and CCTSDB2021 demonstrate that our method demonstrates substantial performance enhancements while introducing only a slight increment in parameter count compared to current algorithms.