# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 1 Introduction
[17] proposed a small object detection method that integrates an adaptive dual-background model with SAO-YOLO, aiming to reduce false positives and false negatives for small and occluded objects. However, this method is not specifically designed for highway scenarios. Despite these advancements, several research gaps remain. First, existing models struggle to balance computational cost and real-time performance, making them unsuitable for real-time highway monitoring. Second, multi-scale detection capabilities are insufficient for complex highway scenarios, where abandoned objects vary significantly in size and distance from the camera. Finally, there is a lack of lightweight detection models specifically designed for highway environments. To resolve these issues, we propose LE-DETR, a lightweight end-to-end highway abandoned object detection model based on the Real-Time Detection Transformer with a ResNet18 backbone (RT-DETR-R18) [18]. Compared to existing methods, LE-DETR introduces a novel lightweight design and efficient multi-scale detection capabilities. Specifically, to address the challenge of balancing computational cost and real-time performance in existing methods, we designed a lightweight and efficient Re-parameterized Efficient Layer Aggregation Network (RepELAN) module for real-time feature extraction. This module reduces the use of residual structures and incorporates the RepConv module with several 3 \(\times\) 3 convolution combinations to simplify the structure and accelerate information flow while capturing more effective data. Based on the RepELAN module, we constructed a Re-parameterized Lightweight Network (RLNet) as a feature extraction backbone for real-time object detection tasks. This network significantly optimizes detection efficiency while maintaining or enhancing detection accuracy. To further enhance the model's multi-scale detection capabilities in complex highway environments, we propose an Efficient Multi-scale Hybrid encoder called the EMHencoder to address significant scale variations in abandoned objects. This encoder consists of two main modules: the Triple Fusion (TFusion) module and the Cross-Layer Multi-Scale Interaction (CMI) module. The TFusion module enriches the representation of detail by combining high-level semantic information from deeper layers with low-level spatial information from superficial layers, thereby improving detection performance. The CMI module enhances the capability to detect multi-scale abandoned objects by merging features across different layers and employing large-kernel depth convolutions of varying sizes to extract features from diverse receptive fields. Finally, to validate our model's performance, we constructed a new dataset of abandoned objects on highways and performed a series of detailed experiments. In summary, the contributions in this paper can be summarized as follows:  
1. We present LE-DETR, an end-to-end abandoned object detection model distinguished by its real-time response capability, efficient processing performance, and lightweight design. 2. We designed an efficient real-time feature extraction module, RepELAN. The experimental results demonstrate that this module surpasses similar real-time detection modules in terms of parameter count, floating-point operations, inference speed, and detection accuracy. Building on this module, we constructed a new feature extraction backbone network, RLNet, which reduces computational load, enhances parameter utilization, and significantly improves detection performance. 3. We introduce an encoder with efficient multi-scale feature extraction capabilities named EMHencoder. It includes two core modules: TFusion and CMI. The interaction effect between these modules optimizes handling multi-scale objects in complex scenarios, resulting in more accurate and efficient detection outcomes in practical applications. 4. We constructed a new highway abandoned object dataset, which includes images of abandoned objects on highways under various angles and lighting conditions.