## 4 Results  
### Comparison Experiment  
#### 4.1.1 Comparative Experiments on the Structure of the Neck Network  
In the target detection model, the neck network (neck) is an important module connecting the backbone network (backbone) and the detection head (head), and its main role is to perform further processing, fusion and enhancement of the features extracted from the backbone network in order to improve the detection accuracy and robustness of the model. With the proposed lightweight target detection model, the design of the neck network has become one of the important factors affecting the performance of the model. The structure and optimization of the necking network is especially critical in terms of small target detection in complex contexts, multi-scale target fusion, and the balance of computational efficiency. In order to evaluate the performance of ELANSIimNeck fully, we designed comparative experiments to systematically compare it with several mainstream neck network architectures. The results are shown in Table 1.  
The experimental results show that our proposed ELANSIimNeck achieves optimal performance in both key detection accuracy metrics, mAP50 and mAP50-95, reaching 0.921 and 0.471, respectively, which are 9.6% and 11.9% higher compared to PAFPN [18] (0.84 and 0.421) and 2.3% and 6.3% compared to HSFPN [19] (0.9 and 0.443) and also better than BiFPN [20] (0.911 and 0.461) and Slim-Neck (0.91 and 0.459). It is shown that ELANSIimNeck can detect targets more accurately under different IoU thresholds, and has stronger robustness and generalization ability in complex environments. In terms of computation, the GFLOP of ELANSIimNeck is only 6.1, which is 13% lower than BiFPN (7.0) and also lower than PAFPN (6.3) and Slim-Neck (6.3) and is close to HSFPN (5.6), which indicates that it effectively reduces computation and improves the inference efficiency while maintaining a high detection accuracy, and is suitable for deployment in resource-constrained devices. In terms of the number of parameters, ELANSIimNeck has only 2,119,891 parameters, which is significantly lower than Slim-Neck (2,734,843) and BiFPN (2,670,343), a reduction of 22.5% and 20.6%, as well as a reduction of 17.9% compared to PAFPN (2,582,347), and is slightly higher than HSFPN (1,845,435), but ELANSIimNeck achieved greater performance gains. From the perspective of FPS (Frames Per Second), the proposed ELANSIimNeck demonstrates a significant advantage in inference speed. Compared to BiFPN (105.1 FPS) and HSFPN (110.3 FPS), ELANSIimNeck improves inference speed by 26.3% and 20.3%, respectively. Additionally, it achieves a 40.2% increase over Slim-Neck (94.6 FPS). This result indicates that our improved approach significantly enhances computational efficiency while maintaining high accuracy, making it more practical for real-time applications. In particular, ELANSIimNeck exhibits greater deployment advantages in resource-constrained embedded devices. Therefore, by optimizing the feature fusion and channel attention mechanisms, ELANSIimNeck achieves better detection performance and lighter structure while maintaining computational efficiency and demonstrates superior performance in a number of metrics compared to PAFPN, BiFPN, HSFPN, and Slim-Neck, proving its superiority in the task of target detection in complex environments. The results of the visualization experiment are shown in Figure 11.