Department of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Drug and Instrument Supervisory and Test Station of Xining Joint Service Support Center, Lanzhou, China.
Front Public Health. 2023 Feb 9;11:999378. doi: 10.3389/fpubh.2023.999378. eCollection 2023.
UAV-based multispectral detection and identification technology for ground injured human targets, is a novel and promising unmanned technology for public health and safety IoT applications, such as outdoor lost injured searching and battlefield casualty searching, and our previous research has demonstrated its feasibility. However, in practical applications, the searched human target always exhibits low target-background contrast relative to the vast and diverse surrounding environment, and the ground environment also shifts randomly during the UAV cruise process. These two key factors make it difficult to achieve highly robust, stable, and accurate recognition performance under the cross-scene situation.
This paper proposes a cross-scene multi-domain feature joint optimization (CMFJO) for cross-scene outdoor static human target recognition.
In the experiments, we first investigated the impact severity of the cross-scene problem and the necessity to solve it by designing 3 typical single-scene experiments. Experimental results show that although a single-scene model holds good recognition capability for its scenes (96.35% in desert scenes, 99.81% in woodland scenes, and 97.39% in urban scenes), its recognition performance for other scenes deteriorates sharply (below 75% overall) after scene changes. On the other hand, the proposed CMFJO method was also validated using the same cross-scene feature dataset. The recognition results for both individual scene and composite scene show that this method could achieve an average classification accuracy of 92.55% under cross-scene situation.
This study first tried to construct an excellent cross-scene recognition model for the human target recognition, named CMFJO method, which is based on multispectral multi-domain feature vectors with scenario-independent, stable and efficient target recognition capability. It will significantly improve the accuracy and usability of UAV-based multispectral technology method for outdoor injured human target search in practical applications and provide a powerful supporting technology for public safety and health.
基于无人机的多光谱探测与识别技术可用于地面受伤人体目标，是公共卫生和安全物联网应用（如户外搜救和战场伤员搜救）中一种新颖且有前途的无人技术，我们之前的研究已经证明了其可行性。然而，在实际应用中，搜索到的人体目标相对于广阔多样的周围环境总是表现出低目标-背景对比度，并且地面环境在无人机巡航过程中也会随机移动。这两个关键因素使得在跨场景情况下很难实现高度稳健、稳定和准确的识别性能。
本文提出了一种跨场景多域特征联合优化（CMFJO）方法，用于跨场景户外静态人体目标识别。
在实验中，我们首先通过设计 3 个典型的单场景实验，研究了跨场景问题的严重程度以及解决该问题的必要性。实验结果表明，尽管单场景模型对于其场景具有良好的识别能力（沙漠场景为 96.35%，林地场景为 99.81%，城市场景为 97.39%），但其在场景变化后的其他场景中的识别性能急剧下降（整体低于 75%）。另一方面，我们还使用相同的跨场景特征数据集验证了所提出的 CMFJO 方法。个体场景和组合场景的识别结果表明，该方法在跨场景情况下平均可达到 92.55%的分类准确率。
本研究首次尝试构建一个优秀的跨场景人体目标识别模型，名为 CMFJO 方法，它基于具有场景独立、稳定和高效目标识别能力的多光谱多域特征向量。它将显著提高基于无人机的多光谱技术方法在实际应用中对户外受伤人体目标搜索的准确性和可用性，并为公共安全和健康提供强大的支持技术。
