在空基对地目标检测背景下,由于对地成像视角单一、目标尺寸随成像高度变化以及背景干扰复杂等,现有深度学习目标检测算法难以取得令人满意的效果。基于此,提出一种重点区域注意力学习机制,用于增强特征图的表达能力,同时缓解复杂背景特征的干扰问题。首先,建立重点区域注意力学习机制,使网络能选择性地关注和利用图像中的目标区域特征;其次,通过设计区域注意和目标检测相耦合的损失函数,实现区域注意损失和目标检测损失的同步优化;最后,利用空对地目标检测数据集进行实验。结果表明,所提算法能有效地关注和利用重点区域的特征信息,减小背景信息的干扰,提高空对地目标检测的精度和抗干扰能力。

Existing depth-learning target detection algorithms are unsuitable for air-to-ground target detection because the results are degraded by the single imaging angle, target size changing with imaging height, and complexity of the background interference. To solve this problem, this paper proposes a attention learning mechanism in key areas, which enhances the expressive ability of the feature maps and alleviates the interference of complex background features. This paper first establishes the proposed learning mechanism, which enables the network to select and utilize the features of the target regions in images. Second, it designs a loss function coupled with regional attention and target detection for synchronous optimization of the regional attention loss and target detection loss, which is then achieved by data mining. The proposed algorithm is experimentally evaluated on air-to-ground target detection datasets. The algorithm effectively focuses on and utilizes the feature information of the target key areas, reduces the interference of the background information, and improves the accuracy and anti-interference ability of air-to-ground target detection.