## 2 Related Research
The findings revealed that YOLO-NAS attained 91.7% of mAP in the goal alignment task. However, it exhibited suboptimal performance in the dynamic target tracking task. This observation underscores the pivotal interplay between model architecture and task suitability, highlighting the critical impact of model architecture and task relevance on performance outcomes. With regard to detector structure optimization, [PERSON] et al. [28] proposed a C3-module-optimized feature pyramid structure based on YOLOv7x [16], which achieves 88.6% mAP on a self-built dataset. However, the computational complexity of 2.4G FLOPs restricts the feasibility of its deployment on mobile. In their seminal work, [PERSON] and [PERSON] [29] proposed a feature pyramid structure using the YOLOv11 model. This structure was developed to compress the number of model parameters to 1.8M through the C3k2 module. The authors then combined this with a dynamic anchor frame clustering algorithm. This combination was implemented to improve the AP50 of the COCO dataset by 2.3%. In order to enhance the detection and tracking of targets by deep learning algorithms, scholars have explored the fusion strategy of lightweighting and feature enhancement in several directions. For instance, the utilization of light field technology has been demonstrated to enhance the feature expression ability and achieve improvement in image processing accuracy [30; 31; 32]. [PERSON] et al. [33] combined block labeling with an adaptive saliency mechanism to effectively improve the detection accuracy of the model. [PERSON] et al. [34] enhanced the model's ability to model fine-grained targets through complex feature pyramid design. Moreover, the incorporation of the attention mechanism, particularly the emergence of lightweight modules, presents a novel opportunity to regulate the computational resource overhead while preserving model performance. The concept of spatial attention was initially introduced by Google [35] in 2018. This theoretical framework simulates the human capacity to allocate cognitive resources to salient regions through an effective weighting mechanism. Spatial attention has found extensive application in various domains, including image classification, target detection, and semantic segmentation, to name a few. The spatial attention module has been developed to target specific regions, thereby enhancing the precision of feature expression. The spatial attention model is concerned with the delineation of salient locations and has been demonstrated to enhance the efficacy of feature representation by directing the network to prioritize critical regions. Notable contributions in this domain include the CBAM module, as proposed by [PERSON] et al. [36], and the BAM structure, as proposed by [PERSON] et al. [37]. Lightweight attention mechanisms, exemplified by SimAM [38] and Shuffle Attention [39], have been shown to prioritize performance optimization in scenarios where computational resources are limited. These mechanisms have emerged as a pivotal research direction for the deployment of edge devices. In recent years, considerable attention has been directed towards channel attention (CA) mechanisms. The ECA (Efficient Channel Attention) module [40] enhances the network's feature selection capability without substantial increases in model complexity through local cross-channel interactions and parameter-free gating mechanisms. This module has demonstrated robust adaptability and generalization in structures such as YOLOv5 [14], YOLOv7 [16], and other structures that exhibit comparable adaptability and generalization. Table 1 summarizes the aforementioned studies. To summarize, although extant studies have achieved a certain degree of success in the detection and tracking of multiple targets in dynamic scenarios, there are two fundamental limitations that persist. Firstly, the majority of systems are predicated on offline analysis and are devoid of real-time feedback functionality.