# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 2 Methodology
### Proposed Methodology for the Detection of Abandoned Objects on Highways
#### 2.2.1 Design of RepELAN and RLNet  
The task of abandoned object detection poses significant challenges in balancing efficiency and accuracy. Although previous research has explored various design strategies, these strategies are still unable to avoid inherent computational redundancies that result in an extra increase in computational overhead. To address this challenge, we  
Figure 1: Structure of the RT-DETR-R18 model.  
Figure 2: Structure of the LE-DETR model.  
propose RepELAN, a lightweight cascading feature extraction module. By optimizing computational architecture and memory demands, this module enhances real-time capabilities, significantly reduces computational resource consumption, and greatly improves detection performance, making it more suitable for deployment on resource-constrained embedded devices.  
As depicted in Figure 3, we designed RepELAN based on the C2f feature extraction module from YOLOv8 and the backbone network concept from RepVGG [20; 21]. To accelerate information flow, we removed the bottleneck layer residual structures from the C2f module and replaced the original \(n\) bottleneck layers with \(n-1\)\(3\)\(\times\)\(3\) convolution layers. This modification increased the detection speed by reducing the model's memory occupation and access costs, as illustrated in Figure 4. Additionally, the NVIDIA library has been specifically optimized for \(3\)\(\times\)\(3\) convolutions. Before the \(n-1\)\(3\)\(\times\)\(3\) convolutions, we introduced RepConv, a training-inference-decoupled convolution, to compensate for potential performance loss from removing residual structures. During training, RepConv enhances the training performance of the model by parallelly performing \(3\)\(\times\)\(3\) convolutions, \(1\)\(\times\)\(1\) convolutions, and identity mappings. It is effectively transformed into a single-path \(3\)\(\times\)\(3\) convolution during the inference stage to optimize inference speed. After applying RepConv and \(3\)\(\times\)\(3\) convolutions, we added a \(1\)\(\times\)\(1\) convolution to produce additional feature maps that improve network performance without incurring substantial computational overhead [22]. Finally, the key component of RepELAN during inference consists of a sequence of \(n\)\(3\)\(\times\)\(3\) convolutions, followed by a solitary \(1\)\(\times\)\(1\) convolution. This design significantly increases the rate of information transmission and decreases the computational burden. In addition, we introduced a scaling factor in the proposed module, allowing for flexible adjustment of the model's size and complexity to suit different application scenarios. In this paper, the scaling factor is set to \(0.5\).  
Figure 4: Comparison of the residual structure with the serial structure, where the residual structure requires the input x to be continuously stored in the memory before the addition operation, doubling the memory occupation.  
Figure 3: Detailed structural comparison of the C2f and RepELAN modules.  
Expanding upon the RepELAN foundation, we constructed the feature extraction backbone network, RLNet, as depicted in the backbone section of Figure 2. Images with dimensions of 640 \(\times\) 640 \(\times\) 3 are inputted into this network, generating a feature map with dimensions of 160 \(\times\) 160 \(\times\) 128 after two convolution operations and one feature extraction process. Subsequently, the network is divided into three stages, with each stage preceded by an Adown module that performs spatial downsampling while retaining more image information. Every stage has a RepELAN module to process and extract feature representations effectively.