# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 1 Introduction  
Highway traffic safety has long been one of the main issues in road safety. However, abandoned objects seriously threaten highway safety. Therefore, the rapid identification and removal of abandoned objects on highways are essential for improving road safety. Manual inspection is the primary method for identifying abandoned objects, but it is time-consuming, ineffective, and unable to meet real-time monitoring needs. Moreover, manual inspection is inappropriate in an environment with rapidly moving traffic. Many traditional techniques for detecting abandoned objects have been presented in recent years. [PERSON] et al. [1] used a Gaussian Mixture Model (GMM) [2] for background modeling and edge statistical methods to eliminate noise and optimize detection using an integrated object-tracking module. However, when the dynamic environment changes heavily, this method does not update frequently enough, delaying the recognition of emerging static objects and reducing overall detection accuracy. [PERSON] [3] proposed a method for identifying lost luggage based on the stability of foreground pixel values. Nevertheless, this method is primarily appropriate for interior environments and is onlyeffective with larger objects, rendering it unsuitable for complex highway scenarios. [PERSON] et al. [4] proposed a detection system that utilizes three-dimensional image information. The system initially employs a monocular camera video to perform preliminary static foreground segmentation to identify suspected abandoned objects. Subsequently, a stereo camera performs 3D reconstruction to evaluate potential traffic threats. However, the effectiveness of this system in dynamic and complex road environments is significantly restricted by its reliance on complex image processing techniques and precise stereo camera calibration. [PERSON] et al. [5] employed a GMM to build a double learning rate background modeling system that analyzes the differences between various frames to identify static or dissipating objects in the foreground. However, due to its high computational cost, this system is inappropriate for application scenarios requiring a prompt response. With the rapid advancement of deep learning technologies, significant progress has been made in applying these methods for detecting abandoned objects on highways. [PERSON] et al. [6] presented a technique that integrates dual-background modeling with Mask R-CNN [7] to detect abandoned and stolen objects from video surveillance systems. This method effectively detects stationary objects and reduces errors induced by variations in lighting, but it is unsuitable for highway scenarios. [PERSON] et al. [8] proposed a method combining MOG2 [9] background modeling and YOLOv9 [10] for abandoned object detection on highways. This approach improves detection accuracy in dynamic environments but needs optimization for handling noise and vehicle occlusion. [PERSON] et al. [11] proposed a novel 20-layer residual network (Nov-ResNet-20) to detect abandoned objects in real-time traffic surveillance videos. This network combines selective search [12; 13] and Non-Maximum Suppression (NMS) [14; 15] to optimize the object detection process and improve abandoned objects' recognition rate and accuracy. Although the method can effectively identify abandoned objects in traffic videos, the processing time of the images remains lengthy, affecting the method's efficiency in practical applications. [PERSON] et al. [16] proposed a lightweight YOLOv5-MN3 convolutional neural network, significantly reducing the parameters and computational costs by employing a depth-separable convolution instead of a standard convolution. Furthermore, the network integrates an attention mechanism to improve the extraction of features and optimizes the model by employing knowledge distillation techniques. While the model successfully attains a lightweight design, the implementation method is more complicated. [PERSON] et al.