Shi Hao, He Cheng, Li Jianhao, Chen Liang, Wang Yupei
Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
Chongqing Innovation Center, Beijing Institute of Technology, Chongqing, China.
Front Neurosci. 2022 Nov 30;16:1074706. doi: 10.3389/fnins.2022.1074706. eCollection 2022.
As a computing platform that can deal with problems independently and adapt to different environments, the brain-inspired function is similar to the human brain, which can effectively make use of visual targets and their surrounding background information to make more efficient and accurate decision results. Currently synthetic aperture radar (SAR) ship target detection has an important role in military and civilian fields, but there are still great challenges in SAR ship target detection due to the problems of large span of ship scales and obvious feature differences. Therefore, this paper proposes an improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism, which efficiently focuses on target information ignoring the interference of complex background. First of all, most target detection algorithms are based on the anchor method, which requires a large number of anchors to be defined in advance and has poor generalization capability and performance to be improved in multi-scale ship detection, so this paper adopts an anchor-free detection network to directly enumerate potential target locations to enhance algorithm robustness and improve detection performance. Secondly, in order to improve the SAR ship target feature extraction capability, a dense connection module is proposed for the deep part of the network to promote more adequate deep feature fusion. A visual attention module is proposed for the shallow part of the network to focus on the salient features of the ship target in the local area for the input SAR images and suppress the interference of the surrounding background with similar scattering characteristics. In addition, because the SAR image coherent speckle noise is similar to the edge of the ship target, this paper proposes a novel width height prediction constraint to suppress the noise scattering power effect and improve the SAR ship localization accuracy. Moreover, to prove the effectiveness of this algorithm, experiments are conducted on the SAR ship detection dataset (SSDD) and high resolution SAR images dataset (HRSID). The experimental results show that the proposed algorithm achieves the best detection performance with metrics AP of 68.2% and 62.2% on SSDD and HRSID, respectively.
作为一种能够独立处理问题并适应不同环境的计算平台，受大脑启发的功能类似于人类大脑，它能够有效地利用视觉目标及其周围的背景信息，从而做出更高效、准确的决策结果。目前，合成孔径雷达（SAR）舰船目标检测在军事和民用领域都具有重要作用，但由于舰船尺度跨度大、特征差异明显等问题，SAR舰船目标检测仍面临巨大挑战。因此，本文提出了一种基于受大脑启发的注意力机制的改进无锚点SAR舰船检测算法，该算法能够有效地聚焦于目标信息，忽略复杂背景的干扰。首先，大多数目标检测算法基于锚点方法，该方法需要预先定义大量锚点，并且在多尺度舰船检测中泛化能力较差，性能有待提高，因此本文采用无锚点检测网络直接枚举潜在目标位置，以增强算法的鲁棒性并提高检测性能。其次，为了提高SAR舰船目标特征提取能力，针对网络深层提出了密集连接模块，以促进更充分的深层特征融合。针对网络浅层提出了视觉注意力模块，用于聚焦输入SAR图像中舰船目标在局部区域的显著特征，并抑制具有相似散射特性的周围背景的干扰。此外，由于SAR图像相干斑噪声与舰船目标边缘相似，本文提出了一种新颖的宽高预测约束，以抑制噪声散射功率效应，提高SAR舰船定位精度。而且，为了证明该算法的有效性，在SAR舰船检测数据集（SSDD）和高分辨率SAR图像数据集（HRSID）上进行了实验。实验结果表明，所提算法在SSDD和HRSID上分别以68.2%和62.2%的平均精度（AP）指标取得了最佳检测性能。
