# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 4 Conclusions  
This study focuses on the existing challenges with abandoned object detection technology, including computational resource consumption, scale variability of abandoned objects, and interference from complex backgrounds. We propose LE-DETR, a lightweight end-to-end model for detecting abandoned objects on highways. Initially, we designed the real-time feature extraction module RepELAN, which surpasses comparable modules regarding the number of parameters, floating-point operation times, inference speed, and detection accuracy. Building upon the RepELAN module's success, we constructed the RLNet backbone network, effectively reducing computational load and improving parameter utilization. Additionally, we designed the TFusion module, which effectively enhances the representation of detailed features by combining high-level semantic information with low-level spatial information. Subsequently, we proposed the CMI module, which improves the multi-scale feature representation of abandoned objects by utilizing large kernels of varying sizes in depth-wise convolution to extract features from different receptive fields in parallel. The EHMencoder was proposed by combining the TFusion and CMI modules, which brought about a significant improvement in the accuracy of detection for multi-scale abandoned objects. Ultimately, we constructed a novel dataset of abandoned highway objects and conducted experiments. The LE-DETR model is more appropriate for resource-constrained devices while maintaining real-time performance, as evidenced by comprehensive results that indicate a 6.5% and 2.2% improvement in AP50.95 and AP50, respectively, and a 27.1% and 21.1% reduction in the number of parameters and FLOPs compared to baseline models. LE-DETR's ability to effectively detect abandoned objects on highways under various complicated circumstances was also demonstrated in the experiments. Although the model proposed in this paper effectively improves the detection efficiency of abandoned objects on highways, it has certain limitations. The dataset primarily includes daytime highway scenes, while nighttime and extreme weather conditions have not been sufficiently considered. The model is only able to identify a portion of abandoned object types that are present in real-world scenarios as a result of the dataset's restricted size. Consequently, future research should concentrate on improving the algorithm's generality to identify various abandoned objects in various scenarios.  
Conceptualization, B.Z. and C.W.; Methodology, B.Z.; Validation, B.Z.; Formal analysis, B.Z. and C.W.; Writing--original draft, B.Z.; Writing--review and editing, C.W. and J.W.; Investigation, C.W. and J.W.; Supervision, J.W. All authors have read and agreed to the published version of the manuscript. This work is partly supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX24_1327). The data presented in this study are available upon request from the corresponding author due to privacy concerns. The authors wish to thank the Jinling Institute of Technology (JIT) for providing computational and storage resources. The authors declare no conflicts of interest.