Automated detection of military people based on the images in different environments plays an important role in accurately completing military missions. With the equipment gradually moving towards intelligence, unmanned aerial vehicles (UAVs) will be widely used for integrated reconnaissance/attack in the future. The lightweight and compact design of the small UAV allows it to travel through dense forests and other environments to capture images with its convenient mobility. However, as the camouflage has been designed to blend in with surroundings, which greatly reduces the probability of the target being discovered. Moreover, the lack of training data on camouflaged people detection will inhibit the training of a deep model. To address these problems, a novel semi-supervised camouflaged military people detection network is proposed to automatically detect the target from the images. In this paper, the camouflaged object detection dataset (COD10K) is first supplemented according to our mission requirements, then the edge attention is utilized to enhance the boundaries based on search identification network. Further, a semi-supervised learning strategy is presented to take advantage of the unlabeled data which can alleviate insufficient data and improve the detection accuracy. Experiments demonstrate that the proposed semi-supervised search identification network (Semi-SINet) performs well in camouflaged people detection compared with other object detection methods.

基于不同环境下的图像对军人进行自动化检测，对于准确完成军事任务具有重要作用。随着装备逐渐走向智能化，未来无人机将广泛应用于一体化侦察/攻击。小型无人机轻巧紧凑的设计使其能够以其便捷的机动性穿越茂密的森林等环境进行拍摄。但是，由于迷彩被设计成与周围环境融为一体，这大大降低了目标被发现的可能性。此外，伪装人员检测训练数据的缺乏将抑制深度模型的训练。为了解决这些问题，提出了一种新的半监督伪装军人检测网络来自动检测图像中的目标。在本文中，首先根据我们的任务要求补充伪装物体检测数据集（COD10K），然后利用边缘注意力增强基于搜索识别网络的边界。此外，提出了一种半监督学习策略来利用未标记的数据，可以缓解数据不足并提高检测精度。实验表明，与其他目标检测方法相比，所提出的半监督搜索识别网络（Semi-SINet）在伪装人员检测方面表现良好。首先根据我们的任务要求补充伪装物体检测数据集（COD10K），然后利用边缘注意力增强基于搜索识别网络的边界。此外，提出了一种半监督学习策略来利用未标记的数据，可以缓解数据不足并提高检测精度。实验表明，与其他目标检测方法相比，所提出的半监督搜索识别网络（Semi-SINet）在伪装人员检测方面表现良好。首先根据我们的任务要求补充伪装物体检测数据集（COD10K），然后利用边缘注意力增强基于搜索识别网络的边界。此外，提出了一种半监督学习策略来利用未标记的数据，可以缓解数据不足并提高检测精度。实验表明，与其他目标检测方法相比，所提出的半监督搜索识别网络（Semi-SINet）在伪装人员检测方面表现良好。提出了一种半监督学习策略来利用未标记的数据，可以缓解数据不足并提高检测精度。实验表明，与其他目标检测方法相比，所提出的半监督搜索识别网络（Semi-SINet）在伪装人员检测方面表现良好。提出了一种半监督学习策略来利用未标记的数据，可以缓解数据不足并提高检测精度。实验表明，与其他目标检测方法相比，所提出的半监督搜索识别网络（Semi-SINet）在伪装人员检测方面表现良好。