Object detection in optical remote sensing images is an important and challenging task. In recent years, the methods based on convolutional neural networks have made good progress. However, due to the large variation in object scale, aspect ratio, and arbitrary orientation, the detection performance is difficult to be further improved. In this paper, we discuss the role of discriminative features in object detection, and then propose a Critical Feature Capturing Network (CFC-Net) to improve detection accuracy from three aspects: building powerful feature representation, refining preset anchors, and optimizing label assignment. Specifically, we first decouple the classification and regression features, and then construct robust critical features adapted to the respective tasks through the Polarization Attention Module (PAM). With the extracted discriminative regression features, the Rotation Anchor Refinement Module (R-ARM) performs localization refinement on preset horizontal anchors to obtain superior rotation anchors. Next, the Dynamic Anchor Learning (DAL) strategy is given to adaptively select high-quality anchors based on their ability to capture critical features. The proposed framework creates more powerful semantic representations for objects in remote sensing images and achieves high-performance real-time object detection. Experimental results on three remote sensing datasets including HRSC2016, DOTA, and UCAS-AOD show that our method achieves superior detection performance compared with many state-of-the-art approaches. Code and models are available at https://github.com/ming71/CFC-Net.
翻译:光学遥感图像中的分析对象探测是一项重要而具有挑战性的任务。近年来,基于进化神经网络的方法取得了良好进展。然而,由于物体规模、侧比和任意定向差异很大,探测性能难以进一步改进。在本文件中,我们讨论了物体探测中歧视性特征的作用,然后提出了一个临界地貌定位网络(CF-Net),以提高从三个方面探测的准确性:建立强大的地貌代表、改进预设锚和优化标签任务。具体地说,我们首先对分类和回归功能进行调和,然后通过极地分关注模块(PAM)为各项任务建立强有力的关键特征。随着分析性回归特征的提取,Rotation Anchor精细化模块(R-ARM)对预先设定的水平锚进行本地化改进,以获得更高级的旋转锚。 下一步,动态Anchor 学习(DAL)战略是根据其捕捉关键特征的适应性地选择高质量的锚。拟议框架为遥感图像和回归力定位模块中的对象创建了更强有力的关键特征,包括遥感图像/网络检测工具,在高水平数据检测方法上,在SALS-DS-S-SARS-SARS-S-S-S-S-S-S-S-S-S-S-S-SARSAR-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SAR-S-S-S-S-SAR-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SAR-SAR-SAR-S-SAR-SAR-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S