# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 2 Methodology
### Proposed Methodology for the Detection of Abandoned Objects on Highways
#### 2.2.3 Implementation Details of the Proposed CMI Module  
The significant scale variations of abandoned objects complicate model recognition compared to common object detection. Accurately identifying abandoned objects depends on their visual characteristics and contextual information. To tackle the issue of significant scale variations, some researchers enhance the model's robustness to scale changes through explicit data augmentation techniques [24; 25; 26]. Moreover, other methods extract  
Figure 5: Detailed structure of the TFusion module.  
rich scale information via multi-scale feature fusion [27; 28] or pyramid architectures [29; 30]. Nonetheless, a constraint of these methods is that the receptive fields for objects of varying scales remain unchanged, limiting their ability to provide adequate contextual information for larger objects. To address the challenges presented by scale variations of abandoned objects, we draw inspiration from PKINet and propose the Cross-Layer Multi-Scale Interaction (CMI) module, which is incorporated into the initial part of the encoder. The CMI module is designed to efficiently extract multi-scale features of abandoned objects across different receptive fields while minimizing the contamination of small object features by large convolution kernels and reducing substantial I/O overhead and latency [31].  
The structure of the CMI module, as shown in Figure 6, adopts an inception-style design [32; 33; 34]. The CMI module merges the feature maps from the backbone network's P3, P4, and P5 layers to generate a fused feature map called \(x\). The map undergoes parallel processing using large-kernel depth-wise (DW) convolutions of sizes \(5\times 5,7\times 7,9\times 9\), and \(11\times 11\). This process effectively captures spatial features of different scales and prevents excessively sparse feature representations. The identity map of feature map \(x\) is integrated with the results of the DW convolutions of varying sizes using element-wise addition. The above process is followed by adding \(1\times 1\) pointwise (PW) convolutions to merge features from various receptive fields and represent channel relationships. To avoid the problem of gradient vanishing, we combine the processed results with the identity map of feature map \(x\) using element-wise addition. This approach ensures that multi-scale features are effectively fused. In the end, the AIFI module utilizes a self-attention mechanism to extract and utilize contextual information from the rich multi-scale feature maps, thereby optimizing detection performance, as demonstrated in Formulas (2)-(4).  
\[x=concat(P_{3}^{\prime},P_{4}^{\prime},P_{5}^{\prime}) \tag{2}\]  
\[Feature=Conv_{1\times 1}(x+\sum_{k\in\{5,7,9,11\}}DWConv_{k\times k}(x)) \tag{3}\]  
\[M=Conv_{3\times 3,stride=2}(x+Feature) \tag{4}\]  
where \(x\) represents the result after processing and merging \(P_{3}^{\prime}\), \(P_{4}^{\prime}\), and \(P_{5}^{\prime}\) and \(k\) denotes the list of sizes for depth convolution kernels. \(Feature\) is the outcome after \(x\) undergoes a series of DW convolutions, feature map additions, and PW convolutions. \(M\) is the result obtained after adding \(Feature\) to the feature map and resizing it.  
Figure 6: Detailed structure of the CMI module.  
When detecting abandoned objects of different sizes, the model exhibits notable accuracy and robustness improvements attributed to the collaborative effect of the TFusion and CMI modules. These improvements demonstrate the EHMencoder's advanced capabilities and effectiveness in practical applications.