Wang Kaipeng, He Guanglin, Li Xinmin
Science and Technology on Electromechanical Dynamic Control Laboratory, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2025 Jun 18;25(12):3800. doi: 10.3390/s25123800.
Target detection in resource-constrained environments faces multiple challenges such as the use of camouflage, diverse target sizes, and harsh environmental conditions. Moreover, the need for solutions suitable for edge computing environments, which have limited computational resources, adds complexity to the task. To meet these challenges, we propose MSCDNet (Multi-Scale Context Detail Network), an innovative and lightweight architecture designed specifically for efficient target detection in such environments. MSCDNet integrates three key components: the Multi-Scale Fusion Module, which improves the representation of features at various target scales; the Context Merge Module, which enables adaptive feature integration across scales to handle a wide range of target conditions; and the Detail Enhance Module, which emphasizes preserving crucial edge and texture details for detecting camouflaged targets. Extensive evaluations highlight the effectiveness of MSCDNet, which achieves 40.1% mAP50-95, 86.1% precision, and 68.1% recall while maintaining a low computational load with only 2.22 M parameters and 6.0 G FLOPs. When compared to other models, MSCDNet outperforms YOLO-family variants by 1.9% in mAP50-95 and uses 14% fewer parameters. Additional generalization tests on VisDrone2019 and BDD100K further validate its robustness, with improvements of 1.1% in mAP50 on VisDrone and 1.2% in mAP50-95 on BDD100K over baseline models. These results affirm that MSCDNet is well suited for tactical deployment in scenarios with limited computational resources, where reliable target detection is paramount.
在资源受限的环境中进行目标检测面临着诸多挑战，例如目标使用伪装、尺寸多样以及环境条件恶劣。此外，对于适用于计算资源有限的边缘计算环境的解决方案的需求，增加了任务的复杂性。为了应对这些挑战，我们提出了MSCDNet（多尺度上下文细节网络），这是一种专门为在这类环境中高效进行目标检测而设计的创新型轻量级架构。MSCDNet集成了三个关键组件：多尺度融合模块，用于改善不同目标尺度下的特征表示；上下文合并模块，能够跨尺度进行自适应特征整合以处理各种目标条件；以及细节增强模块，用于强调保留关键边缘和纹理细节以检测伪装目标。广泛的评估突出了MSCDNet的有效性，它在仅2.22M参数和6.0G FLOP的情况下保持低计算负载，同时实现了40.1%的mAP50 - 95、86.1%的精度和68.1%的召回率。与其他模型相比，MSCDNet在mAP50 - 95上比YOLO系列变体高出1.9%，并且参数使用量减少了14%。在VisDrone2019和BDD100K上的额外泛化测试进一步验证了其鲁棒性，在VisDrone上mAP50提高了1.1%，在BDD100K上mAP50 - 95提高了1.2%。这些结果证实，MSCDNet非常适合在计算资源有限且可靠目标检测至关重要的场景中进行战术部署。
